Technical solutions for customized tours

ABSTRACT

An apparatus and method for configuring a customized tour includes providing a list of tour subject indicators with a relevance value for locations to be toured, receiving user selection data regarding the subject indicators, and configuring a tour route based on an aggregate relevance score calculated for the tour subject indicators and locations indicated by one or more users. The method and apparatus may further include defining at least one tour route record comprising an ordered list of locations that satisfies at least a minimum aggregate relevance score constraint and a maximum tour duration constraint and saving information defining the at least one tour route record in a computer memory for use in delivering a corresponding tour.

FIELD

The present disclosure relates to methods, systems, apparatus forconfiguring customized tours.

BACKGROUND

Places of historical or popular interest attract visitors interested inrelating their knowledge of relevant topics to specific exhibits orportions of the attraction while learning more about what they areseeing. Visitors often have trouble understanding the significance ofwhat they are experiencing during a tour to the relevant topics ofinterest. For example, without guidance it is difficult for visitors toa production studio lot to understand which portions of the lot arerelevant to what the visitor is most interested in, for example, thingsthat relate to specific actors or movie titles. To help visitorsunderstand the significance of their tour experience, tourist often hiretour guides, either directly or through a site manager.

Various methods are used to plan navigation and accompanying informationfor guided or self-guided tours. For example, a tour planner mightdevelop a single tour for all visitors, or several different tours fromwhich visitors can select their preference. In self-guided tours,locating methods can be combined with a library of content to playcontent most relevant to the visitor's current location. This approachcan work well to allow customization of self-guided tours for spacessuch as museums, in which each exhibit relates to a single subject,e.g., a certain work of art or artifact. It does not work as well forattractions in which the same location or object relates to manydifferent topics of interest, or in which the conditions of the toursite are not suitable for self-guided touring.

For example, most locations in production studio lots have been used formany different titles starring even more actors over the years. Eachvisitor may be interested in learning more about different titles orlots. In addition, because studio lots are still used for currentproduction, tour operators cannot allow visitors to go aboutunsupervised. Also, operators usually perform tours for groups and notindividuals, to keep tours more affordable. Thus, visitors to studiolots and similar destinations are often limited to joining a group thatfollows a predetermined path and script that is not customized for theinterests of individual visitors. Connoisseurs of classic movies aregrouped with lovers of soap operas, action-adventure movies, horrorfilms, and all manner of other content produced at the studio over theyears. Consequently, most visitors can learn a little about differenttopics but less about the topics they are most interested in.

It would be desirable, therefore, to develop new methods and other newtechnologies for configuring customized tours that overcomes these andother limitations of the prior art.

SUMMARY

This summary and the following detailed description should beinterpreted as complementary parts of an integrated disclosure, whichparts may include redundant subject matter and/or supplemental subjectmatter. An omission in either section does not indicate priority orrelative importance of any element described in the integratedapplication. Differences between the sections may include supplementaldisclosures of alternative embodiments, additional details, oralternative descriptions of identical embodiments using differentterminology, as should be apparent from the respective disclosures.

In an aspect, a computer-implemented method for configuring a customizedtour of a mapped space for a person, group or cohort may includeproviding, by one or more processors, an ordered arrangement of toursubject indicators configured for output by a user interface device. Asused herein, a “tour subject indicator” is a digital identifier forcontent relating to a subject and to a place or object including in atour. A “user interface device” may refer to a computing apparatus orsystem comprising a processor or cooperating processors configured withexecutable instructions and components for receiving digital inputs fromuser input devices and sensors, processing the digital input determiningtherefrom a digital output from a store of media components, e.g.,video, audio, and other data configured for producing human-perceptibleoutput. Examples include a smartphone (mobile client device), a tablet,a portable computer, a digital streaming player, a smart TV player, anetwork media player, OTT player, a system of mobile smart devices and alocal or remote server, an augmented reality (AR)/virtual reality (VR)headset, or other computing apparatus that produces human-perceivableoutput and can receive user input. A “processor” or “processors” refersto a hardware processor or processors as used in computing apparatus,for example a semiconductor device. In an aspect, each of the toursubject indicators may be associated by an electronic data structure toa relevance value for one or more locations of the mapped space and tocontent about a subject indicated by the tour subject indicator fordelivery at corresponding ones of the one or more locations. In someaspect, the relevance value may indicate a degree of relevance of eachlocation of the one or more locations to the each of the tour subjects.

The method may further include receiving, by the one or more processorsfrom the user interface device, user selection data from a userindicating a lesser subset selected from the ordered arrangement of toursubject indicators.

The method may further include calculating, by the one or moreprocessors, an aggregate relevance score for the lesser subset of toursubject indicators and locations of the mapped space.

The method may further include defining, by the one or more processors,at least one tour route record comprising an ordered list of locationsbeing a lesser subset of all locations in the mapped space thatsatisfies at least a minimum aggregate relevance score constraint and amaximum tour duration constraint.

The method may further include saving information defining the at leastone tour route record in a hardware (e.g., electronic, optical, ormagnetic) memory for use in delivering a corresponding tour.

In an aspect, the calculating may further include aggregating edgevalues of a graph database linking the lesser subset of tour subjectindicators to the locations of the mapped space, wherein nodes of thegraph comprise the tour subject indicators.

In an aspect, the method may further include, by the one or moreprocessors, calculating an estimated tour duration based on the orderedlist of locations, at least in part by summing estimated transit timesbetween each location and estimated linger times at each location. Forexample, in an aspect, the method may further include estimating, by theone or more processors, the estimated linger times at the each locationat least in part based on a predetermined delivery period for thecontent for delivery at the each location.

In an aspect, the defining may further include the calculating theaggregate relevance score for each of alternative tour routes satisfyingthe minimum aggregate relevance score constraint and the maximum tourduration. For example, in an aspect, the method may further includeselecting, by the one or more processors, a selected route from thealternative tour routes based on the selected route having a greatest ofaggregate relevance scores determined by the calculating.

In an aspect, the defining may further include generating the orderedlist of locations in part by eliminating candidate locations for periodsduring which the candidate location is indicated as unavailable. Forexample, the method may further include calculating, by the one or moreprocessors, periods during which the candidate location is unavailableat least in part based on scheduled use of the location by one or moreprior tours.

In an aspect, the method may further include by the one or moreprocessors for each of the tour subject indicators, weighting therelevance value by an interest factor indicating a user's level ofinterest in the indicated subject. For example, in an aspect, the methodmay further include by the one or more processors, determining theinterest factor based on one or more of user input, prior user feedbackfor a cohort matching the user, or electronic data indicating userpreferences. In an aspect, the method may further include modifying theat least one tour record during the tour, based at least in part on theuser feedback. In various implementations, modifying the at least onetour record during the tour can be accomplished in real time (e.g., inless than or equal to 1 minute, in less than or equal to 5 minutes, orin less than or equal to 10 minutes). In another aspect, the method mayfurther include receiving at least a portion of the user feedback asbiometric data indicating a neurological response of the user todelivery of the content, from a biometric sensor positioned to detectthe neurological response. In another aspect, the method may furtherinclude, by the one or more processors, adjusting at least one of therelevance value or interest factor based on user feedback receivedduring or after completing at least a portion of a tour following thetour route record. In another aspect, the method may further includetracking, by the one or more processors, progress of the user along thetour, at least in part by receiving a signal from a wireless transmitterprogressing through the tour with the user. In another aspect, themethod may further include, by the one or more processors, processingthe user feedback by a machine-learning algorithm trained to predict atleast one of the interest factor or the relevance value for specificuser cohorts.

In an aspect, the method may further include generating an electronictour guide for content corresponding to locations indicated by the atleast one tour route record relevant to subjects referenced by thelesser subset selected from the ordered arrangement of tour subjectindicators. For example, the method may further include providing theelectronic tour guide to at least one of a mobile device belonging tothe user, or a mobile device belonging to a tour guide. As anotherexample, the method may further include providing the electronic tourguide to one or more electronic processor included in a vehicle, a mediacart or a wearable electronic device (e.g., a watch, a display device, aheadset, etc.).

In another aspect, the method may further include combining, by the oneor more processors, two or more lesser subsets selected from the orderedarrangement of tour subject indicators each corresponding to a differentuser. For example, the method may further include, by the one or moreprocessors, selecting the two or more lesser subsets based on asimilarity measure exceeding a threshold value. In another aspect, themethod may further include, by the one or more processors, prioritizingtour subject indicators in the two or more lesser subsets for includingin the at least one tour route record, at least in part by an algorithmthat includes an equal number of unique tour subject indicators for eachparticipating user.

The foregoing method may be implemented in any suitable programmablecomputing apparatus coupled to an output device such as a video player,by provided program instructions in a non-transitory computer-readablemedium that, when executed by one or more processors (hereinaftercollectively or individually may be referred to as “processor”), causethe apparatus to perform the described operations. The processor may belocal to the apparatus and user, located remotely, or may include acombination of local and remote processors. An apparatus may include acomputer or set of connected computers installed in a vehicle, orportable devices (e.g., smartphones or notepad computers) coupled to anode or network via an access point in a vehicle or otherwise linked toa vehicle for a specific tour. The user interface device may include,for example, a personal computer, mobile phone, notepad computer,projector, haptic interface, scent dispenser, virtual reality device,augmented reality device, or any combination thereof. In someimplementations, the user interface device may include hardware elementsor configurations located on or in a vehicle, for example: a 4D filmpresentation system and/or any of its components, such as a motiongenerating system or moving seat, e.g., D-BOX seats by D-BOXTechnologies Inc. (Canada) or MX4D® theatre seats by MediaMation, Inc.(Torrance, Calif.); a noise cancellation technology such as QuietBubble™ by Silentium Ltd. (Israel); mixed reality gears and equipments,e.g., a VR vest such as KOR-FX by Immerz, Inc. (Cambridge, Mass.); adisplay screen configuration including one or more displays, tactilegear/interface, olfactory interface, haptic devices, pneumatic devices,hydraulic devices, motorized devices, a port to a mobile phone, or thelike.

As used herein, “media cart” or “vehicle” is defined as a movablephysical body or structure that may house one or more persons includinga passenger/user, and/or a tour guide as defined herein, for example, anautonomous vehicle; a transportation vessel such as a tour bus, tram,elevator, taxi, airplane, bus, etc.; an amusement ride; a kiosk; a house(e.g., a motorhome, a camper, or a traditional home); a mobile officespace (mobile or otherwise), and the like, that may or may not beassociated per se with transportation of people. As used in the presentdisclosure, connected vehicles may be referred to simply as vehicles andmay include various suitable types of vehicles, whether drivenautonomously or driven by a person.

Other elements of the apparatus may include, for example, an audiooutput device and a user input device, which participate in theexecution of the method. An apparatus may include, or may couple to, avirtual or augmented reality device (including xR mixed reality outputdevices that may include augmented and virtual reality outputs), such asa headset or other display that reacts to movements of a user's head andother body parts. The apparatus may include, or may couple to, biometricsensors that provide data used by a controller to control details of thecontent about the tour subjects.

To the accomplishment of the foregoing and related ends, one or moreexamples comprise the features hereinafter fully described andparticularly pointed out in the claims. The following description andthe annexed drawings set forth in detail certain illustrative aspectsand are indicative of but a few of the various ways in which theprinciples of the examples may be employed. Other advantages and novelfeatures will become apparent from the following detailed descriptionwhen considered in conjunction with the drawings and the disclosedexamples, which encompass all such aspects and their equivalents.

BRIEF DESCRIPTION OF THE DRAWINGS

The features, nature, and advantages of the present disclosure willbecome more apparent from the detailed description set forth below whentaken in conjunction with the drawings in which like referencecharacters identify like elements correspondingly throughout thespecification and drawings.

FIG. 1 is a schematic block diagram illustrating aspects of a system andapparatus for configuring a customized tour of a mapped space.

FIG. 2 is a schematic block diagram illustrating aspects of a server forconfiguring a customized tour of a mapped space.

FIG. 3A is a schematic block diagram illustrating aspects of a clientdevice for configuring a customized tour of a mapped space.

FIG. 3B is a schematic block diagram illustrating aspects of a systemand apparatus for configuring a customized tour of a mapped space, whichmay involve controlling output responsive to user selection data andtour data, profile data, and/or sensor data indicating one or more userfeedback (e.g., biometric data or neurological state(s)).

FIG. 3C is a schematic block diagram illustrating an example of a datastructure for relating each of the tour subject indicators to arelevance value for one or more locations of the mapped space and tocontent about one or more subjects indicated by the tour subjectindicators.

FIG. 4A is a schematic diagram showing illustrative features of aportion of a graph database, and interactions of a user with aspects ofa system and apparatus for configuring a customized tour of a mappedspace, in accordance with some embodiments of the disclosure.

FIG. 4B is a schematic diagram showing features of a portion of ametaphoric archeological dig in accordance with some embodiments of thedisclosure.

FIG. 5 is a concept diagram illustrating a tour route of locations andrelated tour subjects, initial location, and terminal location that maybe involved in a customized tour of a mapped space according to one ormore embodiments.

FIG. 6 is a schematic block diagram illustrating an example of a datastructure of tour route records.

FIG. 7A is a schematic block diagram illustrating an example of a datastructure of profile data.

FIG. 7B is a schematic block diagram of the interest factors aspects ofthe data structure of profile data.

FIG. 8 is a schematic block diagram of the weighting of aggregatemeasure of relevance or edge values according to one or moreembodiments.

FIG. 9 is a flow chart illustrating high-level aspects of a method forconfiguring a customized tour of a mapped space.

FIG. 10 is a block diagram illustrating high-level aspects of a systemor apparatus for configuring a customized tour of a mapped space.

FIG. 11 is a flowchart showing an algorithm for defining at least onetour route record comprising an ordered list of locations being a lessersubset of all locations in the mapped space.

FIG. 12 is a schematic block diagram illustrating a data structure foruse by one or more processors for associating tour subject indicators.

FIG. 13 is a flow chart illustrating aspects of defining by one or moreprocessors at least one tour record comprising an ordered list oflocations being a lesser subset of all locations in the mapped space.

FIG. 14 is a flow chart showing an algorithm for combining two or morelesser subsets selected from the ordered arrangement of tour subjectindicators each corresponding to a different user.

FIG. 15 is a schematic diagram showing features of an apparatus forconfiguring a customized tour of a mapped space, showing example aspectsof an electronic tour guide in accordance with some embodiments.

FIG. 16 is a flow chart illustrating aspects of a method for configuringa customized tour of a mapped space.

FIGS. 17-19 are flow charts illustrating further optional aspects oroperations of the method diagrammed in FIG. 16.

FIG. 20 is a conceptual block diagram illustrating components of anapparatus or system for configuring a customized tour of a mapped space.

DETAILED DESCRIPTION

Various aspects are now described with reference to the drawings. In thefollowing description, for purposes of explanation, numerous specificdetails are set forth in order to provide a thorough understanding ofone or more aspects. It may be evident, however, that the variousaspects may be practiced without these specific details. In otherinstances, well-known structures and devices are shown in block diagramform to facilitate describing these aspects.

FIG. 1 illustrates aspects of an exemplary environment 100 in which asystem and apparatus for configuring a customized tour of a mapped spacemay operate. A mapped space may include a physical space in the realworld, or a virtual space in a virtual reality world, or a combinationof both in the case of augmented reality world. One or more locations inthe mapped space may include objects, items, stores, restaurants,buildings, landscape, landmark, film studios, filming locations, etc.,appearing or represented in the mapped space. The method allows forconfiguring tour routes including locations and contents about toursubjects for single users or multiple members of a tour group in sharedspace (media cart) or tandem tours (separate tour groups runningtogether), in the user interface device-server environment 100. Otherarchitectures may also be suitable.

The methods may include using user selection data for one or more usersto define at least one tour route record. User selection data mayinclude, for example, selections made by a user via a user interface,defining subjects or locations of interest to the user. The userinterface may present options for selection by a user and receive theuser selection data via the user interface indicating the user'sselections. In an aspect, the methods may further include using userprofile data to define at least one tour route record. As used herein,“user profile” or “profile data” refers to data descriptive of a touruser, for example: geographic location of the user, the user'sresidence, workplace, or frequently visited places; the user'ssurrounding context such as current news and or trending events; weatherand temperature (e.g., sunny, raining, snowing, daytime/nighttime, hotvs. cold, etc.); personal attributes of the user such asage/sex/height/weight/race; favorite elements of a movie or other mediaproduction such as actors, directors, props, movie scenes, dialogs,lines, fictional characters or other celebrities; user's status in areal or fantasy social network; or other profile information. Userprofile may also include the reward status (points earned in one or morevenues of a franchise (e.g., retail stores, amusement parks, liveevents, etc.) hosted or sponsored by an entertainment company andsubscription status in a subscription-based membership service, e.g.Gold level in DC Universe by Warner Bros. Entertainment Inc. Userprofile information may be used to select or configure one or morelocations of the mapped space or content about tour subjects to satisfyat least one of the user's or users' interests, preferences, or safetyneeds.

The methods may also include using interest factors (e.g., tour data) ofone or more users to prepare and optimize the customized tour. The tourdata include information for configuring a tour route to satisfy userinterests within tour constraints. More detailed examples of tour dataare provided herein below. The tour may be experienced by the one ormore users together, for example, by several people touring in a singlevehicle, or in different vehicles that visit the same locations at thesame times or overlapping times. Preparing and optimizing a customizedtour may include, for example, to selecting or configuring at least onetour route including locations and contents about tour subjects based oninterest factors and other data. In an aspect, at a high level, theinterest factors may include preference criteria regarding the userrelevant to a purpose of a tour, for example time, place, bookinginformation, stated reason, such that the tour locations or contents aredirected to furthering the purpose of the tour, for example, bypreparing the users for the tour destination or revisiting one or moretour subjects during the tour. A purpose of a trip may be determined,for example, based on the type of the mapped space, e.g., a film studio,a theme park, a conference site, an outdoor park or recreation area, asporting facility, a musical performance/concert hall, a mall orcollection of shops, a collection of restaurants, a social event, aholiday celebration, etc. In an aspect, the purpose of the trip may bedetermined or received from a tour application. Further details of theinterest factors as used herein will be discussed below, e.g., withreference to FIG. 7B.

In a network architecture, user feedback (e.g., biometric or sensordata) may be collected and processed locally, and used to control ormodify at least one tour record, which may include providing content(media content) to the user interface device(s) from a network source.In some embodiments, media content may be controlled locally, and logdata provided to a remote server for improving predictive analyticsalgorithms and tracking use. As used herein, “media content” or“content” for tour subject refers to digital computer-readable sensorycontent for generating output from a user interface device (e.g., asmart media player). A “predictive analytics algorithm” or more briefly“predictive analytics,” may include any one or a combination of amachine-learning algorithm, a rules-based predictive modeling algorithm,a statistical algorithm, or other programming method for forecastingfuture or unknown events based on current and historical data.

A processor may provide content to a user via software or hardware orboth during a time spent in a tour, for example, audio and video outputfrom a media cart, smart phone, VR/AR output device, kiosk, motionsimulator or ride, or any other output apparatus the user experiencesduring the tour. Content may include, for example, electronic mediacontent for generating output such as audio, video and text; virtual,augmented or mixed reality (VR/AR/xR) content; vehicle simulation; imageprojection/projection mapping (e.g., on clothing, vehicleinterior/exterior, etc.); surround screen; olfactory or tactilestimulus; artificial intelligence robot (e.g., an electronic tour guide,or a driver/passenger/user avatar or simulated personality such as acartoon character by way of image being displayed, audio being played,etc.); and the like. For example, content may include intra-vehicularsocial applications and games. In some embodiments, the content may beconfigured to support an interactive game involving tour content. Thecontent may include simulation or avatar of a vehicle driver or a tourguide, one or more fellow users, or a companion. For example, thesimulation or avatar may include simulating a tour guide providing aguided tour. In other examples, the content may include a social robotthat can be configured to consider user preferences and tourinformation, such as a persona of the vehicle configuring itself andthen displaying a visage with its unique personality on the vehicle'smain display screen. In some implementations, a user or a tour guide'suser interface device (e.g., mobile device) may contain an applicationthat simulates a tour subject or a character from movies, online video,television, video gaming or other fiction. When the application sensesor receives a message informing it that the tour is progressing, it mayactivate the character simulation and operate the simulation toentertain or inform the user using the voice and mannerisms of thecharacter.

As used herein, users (e.g., passengers in media cart(s) of thecustomized tour) are consumers of tour content. In some embodiments, asystem node may collect real-time user feedback (e.g., neurological orbiometric response data) from users for use in controlling contentoutput. As used herein, a “node” includes a client or serverparticipating in a computer network. As used herein, “user” or “users”include all tour participants. Users of content may react passivelyduring viewing or consumption of the content by natural expression oftheir neurological state.

In an aspect, the methods may include using predictive analytics inprocessing the user feedback, for example, to predict at least one ofthe interest factor or the relevance value for specific user cohorts, orin prioritizing tour subject indicators in one or more lesser subsets ofall locations in the mapped space. As noted herein above, a tour subjectindicator is a digital identifier for content relating to a subject andto a place or object including in a tour. In an aspect, the predictiveanalytics may be used to produce (e.g., select or configure) tourcontents. A predictive analytics process may predict an affinity, e.g.,an interest factor or a relevance value, for a user participating in atour or a group of users in tours running together (tandem tour), basedat least in part on the profile data and/or tour data of the user(s),which may include the preference criteria. The affinity may then becomepart of the passenger profile.

Referring to FIG. 1, a suitable customized tour environment 100 mayinclude various computer servers and other network entities includingclient entities in communication via one or more networks, for example aWide Area Network (WAN) 101 (e.g., the Internet) and/or a wirelesscommunication network (WCN) 102, for example a cellular telephonenetwork, using any suitable high-bandwidth wireless technology orprotocol, including, for example, cellular telephone technologies suchas 3rd Generation Partnership Project (3GPP), 4th generation Long TermEvolution (LTE), 5G fifth-generation cellular wireless, Global Systemfor Mobile communications (GSM) or Universal Mobile TelecommunicationsSystem (UMTS), and/or a wireless local area network (WLAN) technologyusing a protocol such as Institute of Electrical and ElectronicsEngineers (IEEE) 802.11, and equivalents thereof. The servers and othernetwork entities (collectively referred to as “nodes”) may connectdirectly, dynamically and non-hierarchically to as many other nodes aspossible (e.g., as in a mesh network) and cooperate with one another toefficiently route data from/to client devices. The servers may, in analternative, connect to client devices in a server-client structure.Some client devices can also act as servers.

Client devices may include, for example, user interface devices 120,such as smartphones, smartwatches, notepad computers, laptop computers,and mixed reality (xR) headsets (e.g., 123-127); and same or similardevices as well as well special purpose media players and serversinstalled as part of media carts 110 (e.g., 121-122).

Computer servers may be implemented in various architectures. Forexample, the environment 100 may include one or more Web/applicationservers 104 containing documents and application code compatible withWorld Wide Web protocols, including but not limited to HTML, XML, PHPand JavaScript documents or executable scripts, for example. Theenvironment 100 may include one or more data servers 105 and/or cloudserver 103 for holding data, for example video, audio-video, audio,graphical content components of tour content for consumption using aclient device, software for execution on or in conjunction with clientdevices, for example sensor control and neurological state detectionapplications, and data collected from users or client devices. Datacollected from client devices or users may include, for example, useridentity, user profile data, sensor data and application data. Useridentity, user profile, and sensor data may be collected by a background(not user-facing) application operating on the client device, andtransmitted to a data sink, for example, a cloud-based data server 103or discrete data server 105. Application data can include applicationstate data, including but not limited to records of user interactionswith an application or other application inputs, outputs or internalstates. Applications may include software for control of tour contentand supporting functions. Applications and data may be served to one ormore system nodes including media carts 110 (e.g., a tour vehicle) fromone or more of the foregoing servers (e.g., 103, 104, 105) or othertypes of servers, for example, any server accessing a distributedblockchain data structure 106, or a peer-to-peer (P2P) server 130associated with a peer-to-peer network such as a mesh network (includingpartial, full, and wireless mesh networks), such as the P2P server 131that may be provided by a set of user interface devices 123 and 124 in atandem tour, etc., and the like, operating contemporaneously asmicro-servers or clients.

In an aspect, information held by one or more of the data server 105,cloud server 103, a server accessing a distributed blockchain datastructure 106, or a peer-to-peer (P2P) server 130 may include a datastructure of tour locations and contents, which may include, but are notlimited to, media components such as video clips suitable for includingin tour content such as a video. The data structure may relate toursubject indicators to relevance value for one or more locations of themapped space or to content about tour subjects indicated by the toursubject indicators, for example by using edge values of a graphdatabase, and to one or more indicators of semantic meaning relevant toone or more locations of the mapped space and tour contents, and otherunique metadata sets characterizing each of the components. As usedherein, a “media component” or “component” is a discrete package orcollection of data that encodes a component of tour content. Mediacomponents may include, for example, “media segments,” which are sets ofaudio, video, audio-video, or other encodings of sensory output by aninteractive media player having a beginning, end, and duration. An audioclip, a video clip, or an audio-video clip are examples of a mediasegment. In various implementations, media components can include datathat encodes sensible output. For example, media components can includethree-dimensional (3D) model data and related texture data which can beused to produce a media segment when rendered by a processor.

The network environment 100 may include various media carts 110, forexample a human-driven media cart 111, 113, 114; an autonomous orconnected media cart 112, etc., that may be connected to servers via theWCN 102 and/or WAN 101. In general, media cart 110 may include or becommunicably connected to computers used by users to access tour contentprovided via a server or from local storage.

Still referring to FIG. 1, the user interface devices 120 in the networkenvironment 100 may include various portable devices, for example amobile smartphone client of a user. Other user interface devices mayinclude, for example, a notepad client, or a portable computer clientdevice, or a mixed reality (e.g., virtual reality and augmented reality)client device. User interface devices may connect to one or morenetworks. For example, the mobile mesh network 131 may include smallradio transmitters that function as a wireless router. The nodes maycommunicate wirelessly with client user interface devices 123, 124, andwith each other by Wi-Fi or other wireless protocol. For furtherexample, the user interface devices 123, 124, in the media carts 113,114 may connect to servers via a wireless access point (not shown, butmay be similar to router 140) communicably connected to the WCN 102and/or the WAN 101. In some such implementations, the wireless accesspoint (WAP) can be a mobile WAP.

Mesh network nodes may be programmed with software that tells them howto interact within the larger network. By following a set of common meshnetwork protocols, the mesh network nodes may transport informationacross the network by hopping the information wirelessly from one meshnode to the next. The nodes may automatically choose the quickest andmost reliable path in a process known as dynamic routing. In a wirelessmesh network, only one node needs to be physically wired to a networkconnection like an Internet modem. That one wired node then shares itsInternet connection wirelessly with all other nodes in its vicinity.Those nodes then share the connection wirelessly with the nodes closestto them. The more nodes, the further the connection spreads, creating awireless “cloud of connectivity.” In general, client devices may be, ormay include, computers or media players used by users to access contentprovided via a server or from local storage. In traveling media cartssuch as the example media carts 110, use of a mobile mesh networkprotocol may allow nearby media carts to share network bandwidth andinformation more efficiently using different frequencies and cachedcontent.

For example, users in two or more media carts 110 traveling to a populartour destination such as the famous “Sound Stage 17” at Warner Bros.Studio may be interested in the same or similar tour content. Continuingthe example, suppose that the first media cart 113 has alreadydownloaded many media components making up the shared content and comeswithin range of a second media cart 114 in a compatible mesh network131. Then, one or more of the user interface devices 120 (e.g., 123,124, or other user interface devices) may join the mesh network 131. Theone or more user interfaces that have recently joined may cooperate witheach other or the user interfaces of the first media cart 113 totransmit/receive media components or other data without needing to passdata through the WCN 102 or WAN 101. Thus, demands on other networks maybe reduced. Mesh networks may be useful in delivering tour contentbecause client devices in media carts following similar tour routes maybe both more likely to request similar content and to be relatively neareach other.

FIG. 2 shows a tour customization (tour content) server 200 forcontrolling output of tour content, which may operate in the environment100, in similar networks, or as an independent server. The server 200may include one or more hardware processors 202, 214 (two of one or moreshown). Hardware may include firmware. Each of the one or moreprocessors 202, 214 may be coupled to an input/output port 216 (forexample, a Universal Serial Bus port or other serial or parallel port)to a source 220 for user input data. In some embodiments, the data mayinclude biometric data indicating a neurological or neurophysiologicalresponse of one or more users to delivery of the tour content. Viewinghistory may include a log-level record of variances from a baselinescript for a content package or equivalent record of control decisionsmade in response to user neurological states and other input. Theprocessor may control output of tour content responsive to user feedbackindicating a user's neurological state, for example using methods asdescribed for cinematic content in International (PCT) applicationSerial No. PCT/US18/53614, incorporated by reference herein.

The server 200 may track user actions and neurological responses acrosstours for individuals or cohorts. Some types of servers, e.g., cloudservers, server farms, or P2P servers, may include multiple instances ofdiscrete servers 200 that cooperate to perform functions of a singleserver. In some embodiments, the source 220 may be separately oradditionally used for sensor data indicative of tour conditions.Suitable sources may include, for example, Global Positioning System(GPS) or other geolocation sensors, one or more cameras configured forcapturing environmental conditions and/or user configurations in theinterior of the media cart 110 or tandem tours 130, one or moremicrophones for detecting exterior sound and interior sound, one or moretemperature sensors for detecting interior and exterior temperatures,door sensors for detecting when doors are open or closed, sensors fordetecting neurological or neurophysiological response of the users ofthe tour, and any other sensor useful for detecting a tour event orstate of a tour user.

The server 200 may include a network interface 218 for sending andreceiving applications and data, including but not limited to sensor andapplication data used for controlling tour content. The content may beprovided from the server 200 to a user interface device 120 or storedlocally on a client device associated with the user interface device120. If stored locally on the client device, the client device andserver 200 may cooperate to handle sensor data and other user datafunctions. In some embodiments, the client may handle all contentcontrol functions and the server 200 may be used for tracking only ormay not be used at all. In other embodiments, the server 200 performscontent control functions.

The processors 202, 214 of the server 200 may be operatively coupled toat least one memory 204 holding functional modules 206, 208, 210, 212 ofan application or applications for performing a method as describedherein. The modules may include, for example, a communication module 206for communicating with client devices and servers. The communicationmodule 206 may include instructions that when executed by the processor202 and/or 214 cause the server to communicate control data, contentdata, and sensor data with a client device via a network or otherconnection. A tracking module 208 may include functions for trackingtour events using sensor data from the source(s) 220 and/or navigationand vehicle data received through the network interface 218 or othercoupling to a media cart 110 or tandem tour 130. In some embodiments,tracking module 208 may include functions for tracking neurologicalresponse and other interactive data for a user or cohort, for one ormore tours, subject to user permissions and privacy settings.

The modules may include, for example, a relevance scoring (RS) module210. The RS module 210 may include instructions that when executed bythe processor 202 and/or 214 cause the server to perform one or more ofdetermining relevance of a user's subjects of interest to tourlocations. For example, the RS module 210 may apply a rule-basedalgorithm, a heuristic machine learning algorithm (e.g., a deep neuralnetwork, hereinafter “predictive analytics algorithm”) or both, todetermine relevance of tour locations to selected subjects of userinterest. The RS module 210 may perform other or more detailedoperations for determining relevance as described in more detail hereinbelow.

The modules may further include a content configuration process (CCP)module 212. The CCP module 212 may include instructions that whenexecuted by the processor 202 and/or 214 cause the server 200 to performone or more of assembling or configuring tour content for delivery toone or more user interface devices, where the parameters of the tourcontent or media content title may be configured based at least in parton the profile data and/or tour data (including interest factors), andfurther operations as described in more detail herein below forconfiguring a tour route record. In alternative embodiments, the contentconfiguration process or function may be omitted from the server memory204 and provided in the memory of a client device. The memory 204 maycontain additional instructions, for example an operating system, andsupporting modules.

Referring to FIG. 3A, aspects of a user interface apparatus (alsoreferred to herein as client device or client) or tour content player300 for controlling output of tour content responsive to a current stateof the tour are described. In some embodiments, the same computingdevice (e.g., apparatus 300) may operate both as a content consumptionapparatus and as a content configuration server, for example, as a nodeof a mesh network. In such embodiments, the computing device may alsoinclude functional modules and interface devices as described above forthe server 200. In an optional aspect, the user interface apparatus maycontrol output of content responsive to direct user feedback, orindirect feedback indicating a user's neurological state.

The apparatus 300 may be in or on a media cart 110. The client 300 mayinclude a processor 302, for example a central processing unit based on80x86 architecture as designed by Intel™ or AMD™, a system-on-a-chip asdesigned by ARM™, or any other suitable microprocessor(s). The processor302 may be communicatively coupled to auxiliary devices or modules ofthe content consumption apparatus 300, using a bus or other coupling. Insome aspects, the processor 302 and its coupled auxiliary devices ormodules may be housed within or coupled to a housing 301, for example, ahousing having a form factor of a tour guide console in a media cart, anonboard infotainment system inside an automobile (e.g., an entertainmentsystem built in or connected to a passenger seat, the ceiling, or anyother part of a vehicle), a kiosk, an elevator infotainment display, adigital signage, an in-flight entertainment system, an amusement ride,seats in 4D venues, a television, a set-top box, a smartphone, a tablet,wearable goggles, glasses, visor, or other form factors. In variousimplementations, the apparatus 300 can comprise an onboard infotainmentsystem inside an automobile (e.g., an entertainment system built in orconnected to a passenger seat, the ceiling, or any other part of avehicle), a kiosk, an elevator infotainment display, a digital signage,an in-flight entertainment system, an amusement ride, seats in 4Dvenues, a television, a set-top box, a smartphone, a tablet, wearablegoggles, glasses or a visor.

The apparatus 300 can comprise a user input device 324 in communicationwith the processor 302 for providing user control input to a tourcustomization process as described herein. The tour customizationprocess can include outputting video and audio. The output video can beconfigured to be displayed on a conventional flat screen or projectiondisplay device. In some embodiments, the tour customization process maybe, or may include, audio-video output for an immersive mixed realitycontent display process operated by a mixed reality immersive displayengine executing on the processor 302.

User input may include, for example, selections from a graphical userinterface or other input (e.g., textual or directional commands)generated via a touch screen, keyboard, pointing device (e.g., gamecontroller), microphone, motion sensor, camera, or some combination ofthese or other input devices represented by block 324. Such user inputdevice 324 may be coupled to the processor 302 via an input/output port326, for example, a Universal Serial Bus (USB), Bluetooth®, Wi-Fi™, orequivalent ports. Control input may also be provided via one or moresensors 328 coupled to the processor 302. The sensors 328 may include,for example, a motion sensor (e.g., an accelerometer), a positionsensor, a camera or camera array (e.g., stereoscopic array), a brainwavedetector, a biometric temperature or pulse sensor, a touch (pressure)sensor, an altimeter, a location sensor (for example, a GlobalPositioning System (GPS) receiver and controller), a proximity sensor, amotion sensor, a smoke or vapor detector, a gyroscopic position sensor,a plenoptic camera, a radio receiver, a multi-camera trackingsensor/controller, an eye-tracking sensor, an infrared/heat signaturesensor, a microphone or a microphone array. In some aspect, any or allof the sensors 328 may be housed in a single or multiple devices, suchas a smartphone and the like. In some implementations, the sensors 328may be located inside the media cart, outside (e.g., on the exterior of)the media cart, or both. For example, accelerometers, bump cancellingsensors, audio/noise canceling sensors, and/or light canceling sensorsmay be located outside, and position sensors (e.g., sensing position(s)of passenger(s)), depth sensors, gesture sensors, and/or microphone(s)may be located inside the media cart 110. For example, a smartphonedevice, an IoT device, a smart device (e.g., Apple Watch™ by Apple, Inc,Google Home™ by Google, Inc., Amazon Echo™ by Amazon, Inc., etc.) orother network-enabled device may house and provide or augmentfunctionalities of one or more of the foregoing sensors. The one or moresensors 328 may further include, for example, a microphone, array ormicrophones, or other audio input transducer for detecting spoken usercommands or verbal and non-verbal audible reactions to output of thetour content.

In optional aspects, the sensor or sensors 328 may detect biometric dataused as an indicator of the user's neurological state, for example,facial expression, skin temperature, pupil dilation, respiration rate,muscle tension, nervous system activity, or pulse. In addition, thesensor(s) 328 may detect a user's context, for example an identity,position, size, orientation and movement of the user's physicalenvironment and of objects in the environment, motion or other state ofa user interface display, for example, motion of a virtual-realityheadset.

In optional aspects for use with virtual reality or augmented realityoutput, the sensor or sensors 328 may generate orientation data forindicating an orientation of the apparatus 300 or a passenger using theapparatus. For example, the sensors 328 may include a camera or imagesensor positioned to detect an orientation of one or more of the user'seyes, or to capture video images of the user's physical environment orboth. In some aspect, a camera, image sensor, or other sensor configuredto detect a user's eyes or eye movements may be integrated into theapparatus 300 or into ancillary equipment coupled to the apparatus 300.

Sensor data from the one or more sensors 328 may be processed locally bythe processor 302 to control display output, and/or transmitted to theserver 200 for processing by the server in real time, or fornon-real-time processing. As used herein, “real time” refers toprocessing responsive to user input without any arbitrary delay betweeninputs and outputs; that is, that reacts as soon as technicallyfeasible. For example, real time processing can include processing in atime interval less than 10 minutes. “Non-real time” refers to batchprocessing or other use of sensor data that is not used to provideimmediate control input for controlling the display, but that maycontrol the display after some arbitrary amount of delay.

To enable communication with another node of a computer network, forexample the tour content server 200, the client 300 may include anetwork interface 322, e.g., an Ethernet port, wired or wireless, or a4G/LTE/5G cellular communications network interface, or other wirelessnetwork. Network communication may be used for data transfer between theclient 300 and other nodes of the network, for purposes including dataprocessing, content delivery, content control, and tracking. The client300 may manage communications with other network nodes using acommunications module 306 that handles application-level communicationneeds and lower-level communications protocols, preferably withoutrequiring user management.

The apparatus 300 can further comprise a display 320 coupled to theprocessor 302, for example via a graphics processing unit 318 integratedin the processor 302 or in a separate chip. The display 320 may include,for example, a flat screen color liquid crystal (LCD) displayilluminated by light-emitting diodes (LEDs) or other lamps, a projectordriven by an LCD display or by a digital light processing (DLP) unit, alaser projector, a light field display (e.g., support near-eye solutionand far-eye solution, or generate images from different planes a-la waveguide bending), a pass-through display e.g., a head-mounted virtualretinal display or other digital display device. For example, aswitchable electric glass screen that transitions from transparent toopaque, paired with a 4K transparent LCD display, may be used. Exampleof such display device includes the display screens used in “Field Tripto Mars” by Framestore VR Studio(http://framestorevr.com/field-trip-to-mars/). Other digital displaydevices may also be used.

The display device 320 may be incorporated into a part of (e.g., awindow of) media cart (incl. virtual reality headset worn by a passengerinside a vehicle), for example, an autonomous vehicle, an amusementride, an interior of transportation vessels such as an elevator, taxi,airplane, bus, etc., a kiosk, or other immersive display systems. Videooutput driven by a mixed reality display engine operating on theprocessor 302, or other application for coordinating user inputs with animmersive content display and/or generating the display, may be providedto the display device 320 and output as a video display to the user.Similarly, an amplifier/speaker or other audio output transducer 316 maybe coupled to the processor 302 via an audio processor 312. Audio outputcorrelated to the video output and generated by the media player module308, tour content control engine or other application may be provided tothe audio transducer 316 and output as audible sound to the user. Theaudio processor 312 may receive an analog audio signal from a microphone314 and convert it to a digital signal for processing by the processor302. The microphone can be used as a sensor for detection ofneurological state and as a device for user input of verbal commands.

The apparatus 300 may further include a random-access memory (RAM) 304holding program instructions and data for rapid execution or processingby the processor during controlling interactive media content inresponse to trip information or other data. When the client 300 ispowered off or in an inactive state, program instructions and data maybe stored in a long-term memory, for example, a non-volatile magnetic,optical, or electronic memory storage device (not shown). Either or bothRAM 304 or the storage device may include a non-transitorycomputer-readable medium holding program instructions, that whenexecuted by the processor 302, cause the device 300 to perform a methodor operations as described herein. Program instructions may be writtenin any suitable high-level language, for example, C, C++, C#,JavaScript, PHP, or Java™, and compiled to produce machine-language codefor execution by the processor.

Program instructions may be grouped into functional modules 306, 308, tofacilitate coding efficiency and comprehensibility. The modules, even ifdiscernable as divisions or grouping in source code, are not necessarilydistinguishable as separate code blocks in machine-level coding. Codebundles directed toward a specific type of function may make up amodule, regardless of whether machine code on the bundle can be executedindependently of another machine code. The modules may be high-levelmodules only. The media player module 308 may perform operations of anymethod described herein, and equivalent methods, in whole or in part.Operations may be performed independently or in cooperation with anothernetwork node or nodes, for example, the server 200.

FIG. 3B is a schematic block diagram illustrating aspects of a systemand apparatus for configuring a customized tour of a mapped space, whichmay involve controlling output responsive to, trip data, profile data,and/or sensor data indicating one or more user feedback (e.g., biometricdata or neurological state(s)). In an aspect, one or a group of users330 is participating in a tour on a media cart 110. In one example, theusers 330 includes user A, user, B, and up to n number of users that mayfit in the media cart 110. In another aspect, a user is traveling alonein a media cart 110. The user can be accompanied by a tour guide or adriver or be travelling alone in the media cart. As discussed above, themedia cart 110 can be equipped with a user interface apparatus 300. Eachuser provides a respective profile data and tour data, which may beprovided to the processor (e.g., processor 302) at the start of thecustomized tour. In some implementations, biometric sensors 328 aindividually associated with each user in the media cart can providebiometric feedback information about the user. In some implementations,the media cart 110 may be equipped with internal or external cameras 328b that may capture and provide audio-video information about one or morelocations, tour subjects, objects, etc., within sight from the mediacart, or user(s) inside the media cart 110. In an aspect, the media cart110 is equipped with one or more biometric sensors 328 c that providebiometric feedback information about the passenger(s) onboard the mediacart 110. Media cart 110 may also be equipped with a geolocation sensor328 d that provides geolocation and trip information of the media cart110.

Systems, apparatus and methods as described herein may make use of adata structure that relates tour route record in an ordered arrangementof tour subject indicators to one or more relevance value/parameters forone or more locations of the mapped space and to contents about toursubjects, including at least one or more indicators of semantic meaningrelevant to one or more tour subjects. FIG. 3C shows an example of adata structure 350 for relating tour subject indicators each referencedby a tour subject identifier 352 to relevance value/parameters 353 forone or more locations of the mapped space and to contents 364, 366 aboutone or more tour subjects indicated by the tour subject indicator, andthe data structure 350 may also be related to user profile such as useridentity, preference data, interest factors, and/or other data. The datastructure 350 may be implemented as a relational database, a graphdatabase, a flat data table, a distributed ledger, or by any otheruseful computer-readable digital data structure for storing, locatingand accessing related components of binary-encoded data. The datastructure 350 may include self-associated records each including aunique identifier 352 of a content component (e.g., an index number, atour subject identifier, etc.); a duration 354 value (e.g., frames,seconds, minutes, etc.) of a tour content (e.g., duration of eachcontent segment including a predetermined delivery period, estimatedtransit times between each location, estimated linger time at each tourlocation) if applicable; one or more semantic tags 356 relevant to tourlocation (e.g., whether the location is indicated as available orunavailable/accessible or not accessible; scheduled use (time/duration)of the tour locations (e.g., by one or more prior tours), tour subjects,contents, or passenger profile data; technical metadata 358 as needed toselect, configure or produce tour content that includes the contentcomponent or media clips identified by the identifier 352, legalmetadata such as for example license or censorship requirementsapplicable to the content component, and one or more links tonon-transitory, computer-readable copies 364, 366 of the contentcomponent. The copies 364, 366 may be identical copies for redundancy ormore efficient distribution, may be copies in different formats (e.g., alarge/high-res format 364 and a small/low-res format 366) for differentoutput modes, or both. It may be efficient to store the copies 364, 366in a different storage system than the data structure 350 for moreefficient operation of the server 200. For example, the server 200 mayload the data structure 350 into its Random Access Memory (RAM) andaccess the component copies 364, 366 on a remote server or localmagnetic or electronic memory device.

FIG. 4A is a schematic diagram showing illustrative features of aportion of an electronic data structure, e.g., a graph database 400(incl. mesosphere 410, stratosphere 420, and troposphere 430), andinteractions of a user 402 with aspects of a system and apparatus 400for configuring a customized tour of a mapped space 401. In an aspect,the graph database 400 is an electronic data structure in which toursubject indicators are associated to a relevance value for one or morelocations of the mapped space and to content about a subject indicatedby the tour subject indicator for delivery at corresponding ones of theone or more locations, wherein the relevance value indicates a degree ofrelevance of each location of the one or more locations to the each ofthe tour subjects.

The atmosphere can be a metaphor for the electronic data structureillustrated in FIG. 4A. Accordingly, the electronic data structure inFIG. 4A can comprise three layers—an upper level mesosphere 410including data (e.g., image) associated with characters and/or actors, amid-level stratosphere 420 including information regarding movie titles,TV shows or other content associated with the characters and/or actorsin the mesosphere 410 and a lower level troposphere 430 includinginformation regarding different locations in the mapped space 401associated with the information in the stratosphere 420.

For example, referring again to FIG. 4A, the mapped space 401 is theWarner Bros. Studio (Burbank, Calif.), and the user 402 is “Amy,” a tourguest. The customized tour may be conducted by a human tour guide 470,e.g., “Billy,” and tour guests including Amy 402 may ride a media cart460 equipped with user interface devices 480, 485 to view or otherwiseenjoy contents for tour subjects. Amy 402, prior to or upon arriving atthe mapped space 401 to join a customized tour, chooses a number of toursubject indicators such as titles of movies, television shows, orscenes, actors, props, etc. appearing in the movies or television shows,or other personalities such as directors and producers associated withthe tour subject indicators, from a list of selectable tour subjectindicators. In an aspect, one or more processors of a system andapparatus for implementing the method for configuring a customized tourof a mapped space described above (hereinafter collectively “processor”)presents an ordered arrangement of tour subject indicators to a user,from which the user indicates a lesser subset selected from the orderedarrangement of tour subject indicators. For example, in an aspect, Amy402 interacts with a tour terminal 403, which may be a computer device120 communicably connected to the network 100, to choose the toursubject indicators. In another aspect, Amy 402 may use her smartphonedevice 120 to choose the tour subject indicators.

FIG. 4B is a schematic diagram showing illustrative features of aportion of an electronic data structure, e.g., a graph database 490. Asillustrated in FIG. 4B, a tour location 431 of the mapped space 401 maybe associated with different shows at different times. Records in thedatabase may be represented as nodes and connected by edges eachassociated with a relationship value or values that represent a strengthof a relationship between the nodes in each of one or more categories,or in an aggregate measure. Together, a collection of nodes and edgeswith relationship metadata may be referred to herein as an “graphdatabase.” The graph database contains values for each of the edgerelationships on which relevance can be rated, i.e., the “relevancevalue” as discussed herein.

The electronic data structures illustrated in FIG. 4A and FIG. 4B may bestored using one or more of the servers 103, 104, 105, 106, and 131. Theelectronic data structures illustrated in FIG. 4A and FIG. 4B may bestored locally on client device 300, or stored remotely and accessedthrough communications network (e.g., WAN 101, WCN 102, etc.). Theelectronic data structures illustrated in FIG. 4A and FIG. 4B may bestored entirely in one storage location or divided into sections andeach section may be stored at one of the plurality of storage locationsdescribed above. The electronic data structures illustrated in FIG. 4Aand FIG. 4B may be composed of nodes and edges. In some embodiments, theelectronic data structures illustrated in FIG. 4A and FIG. 4B may berepresented as pointer tables. In other embodiments, data structuressuch as trees, bi-directional graphs, buckets, or arrays may be used torepresent the electronic data structures illustrated in FIG. 4A and FIG.4B in storage locations.

In an example as illustrated in FIG. 4A, Amy 402 has chosen Scene 441from Film ‘A’ as a tour subject indicator. Scene 441 from Film ‘A’ is anode associated with a location 431, data for which belongs totroposphere 430 in the graph database 400 of tour subject indicators. Inan aspect, the processor may receive the entry of scene 441 from Film‘A’ as a user selection data from the user 402 via a kiosk 403 or otherclient device, indicating a lesser subset selected from the orderedarrangement of tour subject indicators. Nodes 441 and 443 are relevantto the movie, Film ‘A’, represented by the node 422. Each node may beassociated with edge data defining relationships between nodes. Forexample, nodes 441 and 422 may be associated with edge values (e.g.,metadata) 451, 452, 499 representing relevance to an article about afilming location 443, for example, a set used for scenes taking place inthe city of “Casablanca.” An edge 451 leading to node 441 may branch offan edge 452 connecting the Film ‘A’ node 422 to the tour location node432. Each connecting edge 499, 451, 452 represents relationships (e.g.,relevance scores) from which relevance of the corresponding nodes 422,443, 441 may be computed. In the illustrated example, both the scene 441and the article 443 also relate to studio location 432 and to Film ‘A’422. Film ‘A’ 422 may relate to other tour locations, for example, tosecond tour location 431 via edge 454. Supposing, for example, that tourlocation 431 was used to film scenes in Paris, if scene 441 is in Paris,edge 451 would connect to edge 454 instead of edge 452. Edges may havedifferent relevance scores determined by a scheme. For example, edge 499may have a lower relevance score (e.g., 40%) than edge 451 (e.g., 80%)reflecting a relevance determination may by any suitable method.

In an aspect, edges may connect nodes of different levels (e.g.,categories) within the graph database 400. For example, node 441 mayconcern a scene shot at tour location 432 and as such may be categorizedin the lowest level 430 (e.g., troposphere), while node 422 isassociated with Film ‘A’ and belongs to a middle layer 420 (e.g.,stratosphere). Similarly, node 411 in an upper layer 410 (e.g.,mesosphere) represents an Actor ‘ε’ who played a character in the Film‘A’ 422 and it is connected to the node 422 by edge 453. In someembodiments, nodes may be connected to a plurality of other nodes. Forexample, node 422 is connected to nodes 431, 432, and 443 associatedwith location information of the mapped space 401 in addition to nodes411 and 441.

In some embodiments, the processor may identify a plurality of toursubject indicators as candidate components associated with a userselection data by cross-referencing the user selection data with anelectronic data structure such as the graph database 400 to identify anode corresponding to the user selection data, and then identifyingcandidate components connected to the node corresponding to the userselection data. For example, the processor of the present method mayidentify that node 441 corresponds to the term, scene from Film ‘A’indicated by the user 402. The processor may then determine Film ‘A’ isa candidate component associated with node 441 by the connection viaedges 451 and 452. The processor may further determine that Actor ‘ε’ isanother candidate component via the connection via edge 453.” Similarly,the processor may further determine that the tour locations 431, 432have relevance to Node 441 via the edges 452 and 454, the latter vianode 422.

In some embodiments, edge (e.g., edge 454) may be associated with arelevance value that represents a strength of association. For example,the relevance value indicates a degree of relevance of each location ofthe one or more locations to each of the tour subjects. A higher valuemay correspond to higher strength of association of degree of relevance,while the lower value may correspond to lower strength of association ordegree of relevance. Values may be represented as values on any gradientscale with or without endpoints, or as percent values. In some aspect,the processor may adjust or modify the relevance value based on userfeedback. For example, in an aspect, user feedback may be receivedduring or after completing at least a portion of a tour following thetour route record.

The processor may modify this value in response to receiving userconfirmation or user preference data about an association between theterm corresponding to node 441 and the candidate component correspondingto node 422. For example, the processor may present to a user a queryasking for confirmation of association or preference between the node441 regarding a scene from Film ‘A’ and the tour location 432, andreceive user selection input or other response. In response to receivingthe user input, the processor may determine a relevance value linkingnode 441 to node 431. For example, the processor may determine that edge452 connects nodes 422 and 432, and modify a value associated with edge451 that relates to both node 422 and node 432.

In some embodiments, the value associated with an edge may beincremented for every user who confirms an association between any twonodes of the system 400. In some embodiments, for example, a relevancevalue associated with edge 454 may be changed based on a ratio betweenthe number of users who confirmed an association between nodes 441 and431, and the number of users who denied an association between nodes 441and 431. In some embodiments, the processor may only transmit therelative change in value and an identifier of edge 454. For furtherexample, the processor may decrease a relevance value associated with anedge. For example, the processor may receive a user input of selectionof negative response and determine that the relevance score or scoresbetween nodes 422 and 431 should be decreased. In response to thedetermination, the processor may decrease the value associated with edge454.

In some embodiments, the processor may modify the value associated withan edge in response to information received from electronic dataindicating user preferences, or other data in the servers 103, 104, 105,106, and 131. For example, the processor may receive information throughcommunications network (e.g., 101, 102) that a popular scene in Film ‘A’was filmed at tour location 431, and therefore assign a high relevancevalue to the linkage between Film ‘A’ 422 and the location 431. Inresponse to determining an increased relevance value, the processor mayincrease a recorded relevance value associated with edge 454 bytransmitting the new value to electronic data indicating userpreferences, or other data in the servers 103, 104, 105, 106, and 131.to change the value.

In some embodiments, the processor may determine whether to includecontent associated with a node in tour based on multiplying relevancescores of connecting links, or equivalent mathematical operation, andcomparing resulting score with a threshold. The processor may move thethreshold up or down to include just enough nodes needed to fill anallotted time for the tour. For example, suppose the user 402 requests atour including scene 441, and links 451, 462 each have a value of 90%(0.9). Multiplying the two, the resulting relevance for location 432 is81%. So long as the threshold is less than 81%, the processor willinclude location 432 in the tour. Conversely, suppose link 499 has ascore of only 60%. Then, the threshold will need to be not less than 54%(90% multiplied by 60%) to include information about node 443 in thetour, because to get from node 441 to node 443 requires traversing thelinks 451, 499 with respective relevance values of 90% and 60%. In somecases, the processor may determine that a node does not satisfy aminimum aggregate relevance score constraint and therefore exclude itfrom the tour route record.

For further example, edges may be traversed through the troposphere 430like any other layer. A node 423 for Show ‘B’ is connected to locations432 and 433 on the tour route. Supposing again that a user selects Node441 that is connected to the tour location 432. The processor maymultiply or otherwise process the relevance scores of links 451, 452 and455 to decide whether information regarding Show ‘B’ (node 423) shouldbe included in the tour while stopped at the location 432. Likewise, theprocessor may similarly calculate an aggregate relevance score for edges451, 452, 455 and 457 to determine whether to include informationregarding Actor ‘α’ (node 412), for edges 451, 452, 455 and 458 todetermine whether to include information regarding Actor ‘β’ (node 413),and for edges 451, 452, 455 and 456 to determine whether to include avisit to tour location 433 in the tour. The illustrated configuration ofnodes and edges is for example only; other configurations may also beuseful.

Once the processor has determined tour 500 data by similarly traversingedges for input items of interest to a user or group of users, it maysend the data to a media cart 460 including terminals/displays 480 forpassengers and at least one terminal/display for use by a tour guide470.

FIG. 4B shows additional aspects 490 of electronic data structure (e.g.,graph database 400) corresponding to an “archeological dig” metaphor.This structures services situations in which a tour location relates tomany different people or events of interest occurring over a span oftime 495. The processor using edges 491, 492 and 493 with associatedchronological metadata may determine whether a tour location 431 orother node (e.g., a person or object) is relevant to different topics ofinterest that happened at different times. For example, a tour location431 such as a set or sound stage may have been used in differentproductions over the years, such as a recent relatively long usage inShow ‘B’ 423, an older and shorter use in Show ‘C’ 425, and an oldest,briefest use for a scene of Film ‘A’. In addition to applying relevancescores of edges 491, 492 and 493 as described above, the processor mayuse chronological metadata also. For example, a processor may arrange atour presentation in chronological order (newest to oldest, orvice-versa), or may use age and duration of usage as factors incomputing relevance for a customized tour.

FIG. 5 shows a tour route of locations and related tour subjects,initial location, and terminal location as may be represented by a tourroute record for a customized tour 500 of a mapped space according toone or more embodiments. FIG. 5 illustrates a map of initial location(X₀), tour subject A, B . . . n (X₁, X₂, . . . Xn), and terminallocation (Xz) that may be involved in a corresponding tour 500 accordingto one or more embodiments. In an aspect, Initial Location refers to thelocation x₀ within the mapped space (e.g., 401) where the tourcommences. Tour Subject A refers to a tour subject at Location X₁, TourSubject B to a tour subject at Location X₂, and similarly up to TourSubject n, each of which refers to the location Xn, where all of theselocations reside within the mapped space. In an example, TerminalLocation refers to the drop-off location X_(z) where passengers A, B . .. n will be dropped off once the tour is completed, such that nopassenger will remain in the media carts 110 thereafter. In someembodiments, the pick-up location X₀ may be set by the processor as theinitial location at which the one or more processors schedule initiationof a tour session, and the drop off location X_(z) may be set as theterminal location at which the one or more processors scheduletermination of the tour session. In another aspect, the initial locationand the terminal location may be set by the processor at differentlocations between X₀ and X_(z).

In an aspect, the processor calculates an aggregate relevance score forthe lesser subset of tour subject indicators indicated by the user andlocations of the mapped space. In an aspect, any or all of the TourSubjects A, B, . . . n form a part of the tour route record, as may bedefined by the processor, which includes an ordered list of locationsbeing a lesser subset of all locations in the mapped space that satisfyat least a minimum aggregate relevance score constraint and a maximumtour duration constraint. In some embodiments, a plurality ofalternative tour routes 550 may be defined by the processor, where therespective alternative tour routes may include same tour subjectsscheduled to occur in different orders or at different times, ordifferent tour subjects for comparing to one another.

FIG. 6 is a schematic block diagram illustrating an example of a datastructure of tour route records 600 for use by one or more processors inthe method for configuring a customized tour of a mapped space,illustrated as a text data table representing tour data regarding thetour subjects relevant to alternative tour routes, for example time,place, availability, stated reason or derived purpose of the tour, suchthat the tour content components are configured based on the tour data.The table may be organized as a graph database, a relational databasemodel, a self-referential database model, or any other suitable modelknown in the art or that may come to be known in the future. In anaspect, a first row holds tour subject identifiers, such as the name ofthe tour subject or a unique ID assigned to the tour subject. In anaspect, a second row holds the location information for the respectivetour subjects A, B . . . n identified in the first row (ID). In anaspect, a third row may hold content information about the tour subject,such as media files (a, b, . . . n) or script data (aa, bb, . . . nn). Afourth row may hold duration information during which the user or usersparticipating in the tour is expected to linger at the respective toursubject location. For example, the duration information may be derivedby the processors based on a predetermined delivery period for thecontent for delivery at the each location. A fifth row may hold gradeinformation, which includes a relevance value that indicates a degree ofrelevance of each location to each tour subject indicated for deliveryat the each location. A sixth row may hold current accessibilityinformation about the respective candidate locations. For example, thecandidate location may be marked unavailable based on a scheduled use ofthe location by one or more prior tours.

FIG. 7A is a schematic block diagram illustrating an example of adatabase structure of profile data 700 for use by one or more processorsin the method for configuring a customized tour of a mapped space, whichmay be modeled as a text data table like the example of the data tablefor the tour date records 600 discussed above with reference to FIG. 6.The table may be organized as a graph database, a relational databasemodel, a self-referential database model, or any other suitable modelknown in the art or that may come to be known in the future. Thedatabase of profile data 700 may include any or all of the profilingdata pertaining to users as previously defined herein, and inparticular, the database of profile data 700 may include accountidentifier for user 701, past preference (user feedback) data 702,present preference (user feedback) data 703, and interest factors 750with respect to each user with the account identifier 701. If an accountidentifier for a user does not exist or is lost for a given user, onemay be created anew, including present preference data and interestfactors, some or all of which may be automatically populated withpredefined default datasets by the processor.

In an aspect, the profile data for a user may include data bitsrepresenting at least the user identity (account identifier), userfeedback (incl. past and present preferences), and interest factors. Asused herein, the interest factors are a subset of a profile data for auser, and it is defined as electronic data that describes thepreferences of one or more users for tour content, and may include, forexample, favorite tour locations, actors, characters, types ofentertainment, genres, colors, color scheme, music, and so forth.

FIG. 7B is a schematic block diagram of the interest factors (preferencecriteria) aspects 750 of the database structure of the profile datadatabase 700. In an aspect, the interest factors or preference criteriamay be used by the processor to assign or deduce present preference dataof the user identified by the account identifier. Preference criteria750 may include: purpose of the tour, such as outing with friends,co-workers, date, family, etc.; time budget (maximum tour duration) ofthe tour; budget (cost); pace; mood, shared affinities, and userattributes such as age and gender. Once a user experiences a tour 500(or any of the alternative tour routes 550; FIG. 5), the preferencecriteria assigned or deduced as the present preference data will bemoved to the past preference data bucket of the respective user, andpast preference data may be maintained as a log history in the databaseof profile data 700.

FIG. 8 is a schematic block diagram of the weighting of aggregatemeasure of relevance values/edge values according to one or moreembodiments. In an aspect, the processor may calculate the aggregaterelevance score (rv) as a function of the weighting criteria/interestfactors 750 (w) to derive a common interest in tour content or in a tourtandem tours. In another aspect, the processor may use the weightedaggregate measure of relevance values/edge scores to determine thepreference criteria with highest priority for users participating in thetour or tandem tours.

Having described examples of suitable clients, servers, and networks forpreparing an optimizing a customized tour of a mapped space, moredetailed aspects of these methods will be addressed. The apparatus 200and 300 may each perform the methods, alone, or working in cooperation.

FIG. 9 illustrates an overview of the methods 900 for preparing andoptimizing a customized tour of a mapped space, which may includerelated operations in any functional order or in parallel that may beperformed by one or more processors (e.g., 202, 214, 302, 310).References will also be made to (elements appearing in) preceding FIGS.1-8 and to elements referenced in the subsequent FIG. 10 in discussingFIG. 9.

A customized tour optimization method begins at process 910, when one ormore users (330: FIG. 3B) are detected as having initiated participationin a tour (e.g., 500: FIGS. 4 and 5), for example, by a user 402touching a screen 403 for making selection of tour subject indicators(See FIG. 4A). In some embodiments, the processor accesses a database(e.g., any one or more of 116, 122, 124, 126, 128, 220, etc.) includingtour subject indicators for the tour upon detecting the user. In someembodiments, the processor receives signals indicating user profile data(e.g., 700: FIG. 7; 1010: FIG. 10) and tour data (e.g., 1015: FIG. 10)of the user or users participating in the tour.

At the process 920, one or more processors provide an orderedarrangement of tour subject indicators (e.g., nodes in 400 such as411-413, 441, etc.) configured for output by a user interface device(e.g., 403: FIG. 4A; 120: FIG. 1). In an aspect, each of the toursubject indicators is associated by an electronic data structure (e.g.,400) to a relevance value (e.g., 451-459) for one or more locations(e.g., 431, 432, 433) of the mapped space 401 (FIG. 4A) and to content(e.g., 441-443) about a subject indicated by the tour subject indicatorfor delivery at corresponding ones of the one or more locations (e.g.,431-433, and other locations in mapped space 401). In an aspect, therelevance value indicates a degree of relevance of each location of theone or more locations to the each of the tour subjects.

In some implementations, the user profile may include digitally encodeddata bits concerning affinity information related to the user, such as:the user's favorite celebrities, favorite movies or television shows,fictional characters or amusement rides; social graph, status or topicfor a meeting or social event preparation; activity level orlikes/dislikes for outdoor activity; favorite teams or players (e.g.,professional sports teams/players, fictional teams/characters, etc.);favorite artists or songs; preferred merchandise and shopping style(e.g., cartoon character goods, or shopping at a particular retailstore, etc.); culture; age; personal interests; and interactive contentpreferences (e.g., for holiday, season, religion, etc.), and the like.

In some aspect, the one or more processors at a tour optimization server(e.g., 104, 105, 106, 131, 200, etc.) may maintain a data structureholding an ordered arrangement of tour subject indicators associated tocontent components for tour subjects. The data structure may be of anyuseful type, for example as described herein above in relation to FIG.3C. In an aspect, the content may include an interactive and/or definednarrative such as audio video clips explaining the significance of atour location to a subject of interest, or digital data for generatingcustomized content in real time. The data structure may further includean ordered arrangement of components for the tour content.

The profile data 700 and the tour data 1015 may be received or retrievedfrom one or more of the servers 104, 105, 106, 131, 200, and the like.In some aspect of the present disclosure, the sources of the userprofile 700 and tour data 1015 may include another server, or from anapplication (or “app”), or from a third-party server. For example, thesource may be a server or an application from a tour company.

In an aspect, the tour data 1015 may include data bits representing oneor more of the purpose of the tour, tour locations/destinations, anestimated tour duration, estimated transit times between each location,estimated linger times at each location, predetermined delivery periodfor the content for delivery at each location,availability/accessibility of the location, and scheduled use of thelocations (e.g., by one or more prior tours). The tour data may containinformation representing geospatial locations of one or more datatargets, and may include, for example, positional coordinates such asthe latitude, longitude, and height relative to an ellipsoidal Earthmodel as may be provided by a satellite-based radio navigation systemsuch as the Global Positioning System (GPS), street address, name (e.g.,landmark or building names such as the Sound Studio 7, Warner Bros.Studio, Yosemite National Park, Half Dome, San Francisco Golden GateBridge, Stanford Shopping Mall, city names, etc.), street view (e.g.,Street View™ available on Google Maps™, etc.), and the like of the datatarget.

At the process 930, the method may include receiving from the userinterface device, user selection data from a user indicating a lessersubset selected from the ordered arrangement of tour subject indicators.In one aspect, a user uses her user interface device 120 (e.g.,smartphone) communicably connected to the tour optimization server 200to participate in a tour 500 of a mapped space 400 using a tour app totake a tour (e.g., by media cart 110, by foot, on a rideshare, any othersuitable transportation means, or a combination thereof) to select alesser subset of tour subjects within the mapped space. As part ofcommunicating with the tour optimization server 200, user profile data1010 and/or tour data 1015 may be collected from the smartphone by theprocessor 202, 214, 302, or 310. For example, at least the tourdestinations and timing (tour start time, location durations, andestimated or actual end time) may be collected. In other embodiments,the user profile 1010 and/or tour data 1015 may be collected or inferredfrom input by the user via U/I 324, or from available information on theinternet including social media information pertaining to the user(e.g., social media accounts, etc.). In certain embodiments, userprofile 1010 may include data bits that signify mood (discussed in moredetail below), desired tour experiences, user watchlists or alert lists,news and current events, and other information of evident interest.Similarly, the user profile 1010 and tour data 1015 may be collectedwith respect to any of the other users associated with other userinterface devices 120 (involving other use examples, for example, wherea group of users such as friends or family are participating in the sameor tandem tours) using similar or other means that may be appreciated bythose having ordinary skill in the art.

At the process 940, the method may include calculating an aggregaterelevance score for the lesser subset of tour subject indicators andlocations of the mapped space. For example, calculating an aggregatedrelevance score includes the processor performing an aggregatingfunction over a set of values (tour subject indicators), such as “GROUPBY” in SQL, or “avg( )” in Cypher. Other suitable aggregating functionsmay be used, for example, those which are discussed by Neo4j, Inc. (SanMateo, Calif.) athttps://neo4j.com/docs/cypher-manual/current/functions/aggregating/index.html.In an aspect, the calculating includes aggregating edge values of agraph database linking the lesser subset of tour subject indicators tothe locations of the mapped space, wherein nodes of the graph comprisethe tour subject indicators. In such case, for example, the processormay calculate the average of multiple relevance scores for the lessersubset of tour subject indicators and the locations of the mapped space.In another aspect, the method may include calculating an estimated tourduration based on the ordered list of locations, at least in part bysumming estimated transit times between each location and estimatedlinger times at each location. For example, in an aspect, the method mayfurther include estimating the estimated linger times at the eachlocation at least in part based on a predetermined delivery period forthe content for delivery at the each location.

The one or more processors at process 940 may use a customized touroptimization algorithm 1030, which may be a rule-based algorithm, apredictive analytics (AI) algorithm (tour content AI), or a combinationof both, to calculate aggregate relevance score for tour subjects andlocations. Further details of the structure and operations of thecustomized tour optimization algorithm 1030 are discussed below withreference to FIGS. 13A-13C.

In some embodiments, the method 900 executed by the one or moreprocessors running the customized tour optimization algorithm respondsto user feedback, such as biometric data indicating a neurologicalresponse, words uttered or intents otherwise expressed (e.g., via textinput or intentional gestures) by one or more users and detected by theuser interface devices 120 via the U/I 324, microphone 314, sensors 328,etc., and modify at least one tour record during the tour. For example,a user 402 (Amy) taking a tour 500 while riding a media cart 460equipped with a virtual display device 480, 485 may utter a voicecommand, “Skip ‘Show ‘B’ Stage’ Spiel during the middle of the tourwhile listening to the spiel at Sound Stage 15 (node 432), because shefound it boring. In response, the customized tour optimization processmay modify the tour record, by, for example, ending “The ‘Show ‘B’”spiel abruptly or early, or substituting it with a shorter version ofthe spiel.

At the process 950, the one or more processors may process the userfeedback from the one or more users by a machine-learning algorithm suchas the predictive analytics algorithm 1030 to predict elements of thetour content likely to appeal to the detected user(s), and/or toconfigure mood-sensitive content for the tour 500, which may be loopedback to the process 940 to further refine the process of calculating theaggregate relevance store for tour subjects and locations. For example,the processor at process 950 may use a predictive analytics algorithm tocorrelate biometric data for a user, or her cohort(s) in the group ofusers sharing the tour, to an neurological indicator. It should be notedthat instead of or in conjunction with the biometric data 1020, any oneor more of the passenger profile data 1010, preference criteria 750, andtrip data 1015 may be used, and instead of or in conjunction with theneurological indicators 1040, other sensory data detected by sensors328, or data input by a user (passenger) via U/I 324 or 314 and the likemay be used. For example, the predictive analytics algorithm may beconfigured to process context-indicating data in addition to biometricdata, which may improve accuracy. Context-indicating data may include,for example, user location, user position, time-of-day, day-of-week,ambient light level, ambient noise level, and so forth. For example, ifthe user's context is full of distractions, biofeedback data may have adifferent significance than in a quiet environment.

At the process 960, the one or more processors defines a tour routerecord, which records a plan for the tour and subjects to be coveredduring it. The record may be in human-readable format, machine reachableformat, or both. To define the record, the processor may operate analgorithm that receives the aggregate relevance scores from upstreamprocess 940 and determines a tour route that optimizes the utility ofthe tour for the tour group, in view of current resources. The algorithmmay include determining current limitations on the tours, includingavailability of locations and time limits, and finding a highest-scoringtour that can operate within the determined resource constraints. A moredetailed example of an algorithm is described herein below in connectionwith FIG. 14.

The method 900 may select from alternative tour content, based oncomparing neurological indicators to a targeted response for theuser(s). A participating control node may make predictions using machinelearning tools to predict tour content elements likely to produce atargeted neurological state in the user or cohort. Once making theprediction, the control node selects the content scored as most likelyto produce the targeted response. In addition, or in an alternative, thecontrol node may select tour content based on a user's direct input, forexample by weighing direct input together with neurological indicators.Direct user input may include, for example, spoken or texted verbalinput, input from a biometric sensor 328 a or other sensors (e.g., 328c), bodily movement detected by a camera array 328 b, or selection ofcontrol links in a user interface device 120.

In an optional aspect, a processor may alter tour content to keep tourusers engaged. For example, content alterations for engagement at abasic level involve changing the music and SFX volume/mixing tore-engage. Brightness of the screen may also be adjusted. In someimplementations, mood-sensitive content components may include scary orviolent contents that form a part of the tour content, and such contentsmay be cut, censored, or deselected as part of customized touroptimization process 900 when the users include small children and thebiometric data collected from the users indicate fear or aversity.Alternate segments may be identified and stored in a database describedherein for this function.

As shown in FIG. 10, a system 1000 for configuring a customized tour ofa mapped space may use a customized tour optimization process 1030,e.g., a predictive analytics algorithm using artificial intelligence(AI) to detect correlations between tour subject indicators and one ormore locations of the mapped space and content about tour subjects.Predictive analytics, including machine-learning algorithms sometimesreferred to as artificial intelligence (AI), can be an efficient toolfor uncovering correlations between complex phenomena. The customizedtour optimization process 1030 may receive user selection data 1005,user feedback/profile data 1010, and/or tour data 1015 that may betime-correlated to the biometric data 1020 from client devices (e.g.,user interface devices 120, clients 300, and the like). The data may beassociated with a specific user or cohort in the group of usersparticipating in the common tour, or the data may be generic. Both typesof input data (associated with a user/group and generic) may be usedtogether. Generic input data can be used to calibrate a baseline forneurological response, to classify a baseline neurological response to alocation or arrangement of tour subjects including tour contentelements. If most users exhibit similar biometric tells when viewing orexperiencing tour content within the tour context, the tour contentcomponent can be classified with other components that provoke similarbiometric data from users. The similar tour content components may becollected and reviewed by a human tour curator, who may score thecomponents on neurological indicator metrics 1040 manually, assisted byautomated analysis tools. In an alternative, the measure of userinterest factors/relevance value 1040 can be scored by human andsemi-automatic processing without being classed with similar components.These human-scored elements become training data for the predictiveanalytics process 1030. In some embodiments, humans scoring elements ofthe tour content may include the users, such as via pot-tour surveys,reviews, etc. In some aspect, scoring should consider culturaldemographics and may be informed by expert information about responsesof different cultures to scene elements.

The predictive analytics process 1030 is optional. If used, it compareshuman and machine-determined scores of components or other tour contentelements and uses iterative machine learning methods as known in the artto reduce error between the training data and its own estimates.Analysts may score data from multiple users and vehicle trips based ontheir professional judgment and experience. Individual users may scoretheir own content. For example, users willing to assist in trainingtheir personal “tour software” to recognize their neurological statesmight score their own feelings (e.g., interests, affinities or dislikes)or the relevance of produced content to tour information while consumingthe tour content. A combination of these and other approaches may beused to develop training data for the predictive analytics process 1030.Once the process has learned correlations for a passenger or group ofpassengers, it is ready to apply its learned correlations duringreal-time content consumption.

FIG. 13A shows aspects of an algorithm or method 1300 for calculating anaggregate relevance score for the lesser subset of tour subjectindicators and locations of the mapped space. At 1302, the processor maycalculate an aggregate relevance score for the lesser subset of toursubject indicators selected by user, and locations of the mapped space.As used herein, “aggregated relevance score” may include an average ofthe relevance scores that may be derived by a defined procedure orfunction of that may be operable with respect to a given database, suchas AVG( ) in Cypher. In an aspect, the process 1300 may retrieverelevance scores of all tour subject indicators and take an averagethereof. In an aspect, calculating may include aggregating edge valuesof a graph database linking the lesser subset of tour subject indicatorsto the locations of the mapped space, wherein the nodes of the graphcomprise the tour subject indicators as explained in connection withFIG. 4A.

At 1304, the processor may determine whether user feedback is receivedfrom the user before or during completing at least a portion of a tourfollowing a tour route record. Optionally, user feedback may includebiometric data as described elsewhere herein. If the user feedback isreceived, at 1306, the processor may determine whether at least one ofthe relevance value or interest factor based on user feedback receivedbefore or during completing at least a portion of a tour following thetour route record should be adjusted. For example, the user feedback maybe a biometric data indicating continued satisfaction with the tour orthe tour content being presented or recommended to the user, in whichcase the interest factor or the relevance value may stay the same. Inanother aspect, the user feedback may be such that it indicates that theuser is not satisfied or otherwise happy with the tour content beingpresented or recommended to the user, in which case the interest factoror the relevance value for the tour content may be reduced. In anotheraspect, the user feedback may be such that it indicates that the user ishighly satisfied or otherwise very happy with the tour content beingpresented or recommended to the user, in which case the interest factoror the relevance value for the tour content may be increased. When theinterest factor or the relevance value is changed (e.g., reduced orincreased), the change may be reflected in the data structure 350, andthe process may loop back to 1302. And, as further discussed below withrespect to FIG. 13C, the tour route record may be modified by theprocessor based at least in part on the user feedback during the tour.

At 1308, the processor may calculate an estimated tour duration based onthe ordered list of locations. In an aspect, the estimated tour durationmay be calculated at least in part by summing estimated transit timesbetween each location and estimated linger times at each location. In anaspect, the estimated transit times between each location and estimatedlinger times at each location may be retrieved from the data structure350.

At 1312, the processor may determine whether alternative tour routesexist, by calculating aggregate relevance scores for other possible tourroutes and determining whether any of the aggregate relevance scoressatisfy a minimum aggregate relevance score constraint and a maximumtour duration. For example, referring back to FIG. 4A, in an aspect, theminimum aggregate relevance score constraint may be configured as “90%,”in which case the processor may eliminate candidate locations (andcontents about tour subjects) that are ranked below 90% in relevancevalue, e.g., 457-459, and 499, particularly if the average of therelevance scores would fall below 90% for the possible tour routesincluding such candidate locations (and contents about tour subjects).In another example, in case of the user 402 (Amy), whom the processorhas determined as being interested in Film ‘A’ (e.g., from statedinterest indicated by Amy, or as determined by the customized touroptimization Algorithm/AI, etc.), the processor may calculate analternative tour route that includes nodes (locations and/or contents)422, 411, 431, and 432, but may exclude other nodes. Further, supposethe minimum aggregate relevance score constraint is set at “90%” as inthe previous example, and the maximum tour duration is set at “2 hours,”for example, by user preference or input. In an aspect, if the aggregaterelevance score for just-described the tour route for Amy satisfies theminimum aggregate relevance score constraint and the maximum tourduration (e.g., the tour is estimated to take only 90 minutes), then theforegoing tour route is determined as one of the viable alternative tourroutes (e.g., “Amy's tour route,” a selected route). Furthermore,suppose that another alternative tour route includes, in addition toAmy's tour route, a node 443. Such alternative tour route would not havethe greatest of aggregate relevance scores determined by the calculating(“Amy's alternative tour route”)

At 1314, the processor may select a selected route from the alternativetour routes based on the selected route having a greatest of aggregaterelevance scores determined by the calculating. For example, Amy's tourroute may be selected as the selected tour route instead of Amy'salternative tour route, in the examples described above.

At 1316, the processor may again determine whether a user feedback isreceived from the user before or during completing at least a portion ofa tour following a tour route record in a manner similar to process 1304above.

At 1318, the processor may determine whether the tour isended/completed, for example, by determining whether the estimated orallotted tour duration has passed, whether the tour guide has announcedconclusion of the tour, whether the user(s) has indicated conclusion ofthe tour, whether the media cart(s) carrying the user(s) have reached apredetermined tour terminal location or otherwise become unavailable fortour use, etc. If the tour is not ended, the process may loop back to1316.

At 1320, after the tour is ended, the processor may determine whether auser feedback is received from the user before or during completing atleast a portion of a tour following a tour route record in a mannersimilar to process 1304 above. However, this time, the user feedback isnot directly reflected in the tour route (locations and/or contents),and instead, the user feedback may be used by the processor to optimizea future tour for the user.

FIG. 13B shows an example of a data structure 1350 for use by one ormore processors for defining a tour route record including an orderedlist of locations being a lesser subset of all locations in the mappedspace that satisfies at least a minimum aggregate relevance scoreconstraint and a maximum tour duration constraint, including auser-perceivable characteristic, for example a tour subject, contentabout a tour subject, a location, visible place/set, character or objectappearing in or referenced by the tour content component, or a durationof play of the tour content component with a tour data, for example anestimated tour duration based on the ordered list of locations,estimated transit times between each location, estimated linger times ateach location, a change in estimated time remaining until arriving at,or user selection of, a destination, an origin, or an intermediatelocation along the route, predetermined delivery period for the contentfor delivery at each location, availability/accessibility of locations,scheduled use of the locations (e.g., by one or more prior tours), etc.Elements of the data structure 1350 shown in FIG. 13B may be linked toany tour subject identifier by a data structure 350 as shown in FIG. 3C,or equivalent data structure having elements corresponding to one ormore of the elements 1352-1356. The elements 1359-1366 may be genericwith respect to any particular tour or user (or a group of users), andso may be used to correlate a tour content component to multiple toursby multiple users. For example, a processor may correlate a tour contentcomponent with a duration of 15 minutes (i.e., a particular duration)and a semantic tag of “Sound Studio 7” (i.e., a particular semantic tag)in the data structure 350 to any tour by any user along a tour routethat includes at least one destination tag of “Sound Stage 7” (i.e., amatching tag) involved in a tour in a media cart 110 with a duration of15 minutes or longer. The data structure 1350 may be of any suitabletype, for example as described in connection with data structure 350(e.g., graph database) herein above.

The data structure 1350 may include elements pertaining to a tour anduser or a group of users. A processor of a client device or server mayset certain elements, for example, for each user or a group of users,the user ID 1352 tied to the respective profile data 700 (which mayinclude interest factors/preference criteria 750), tour ID 1354 tied tothe respective tour data 1015, and initial location name included in theinitial location tags 1360 and a terminal location name included in theterminal location tags 1366, based on a tour requested by the user(s)(e.g., from a tour personalization application that may be executedand/or displayed on a terminal 403, or user interface devices 120,etc.). Other elements may be populated automatically by a server. Forexample, the customized tour optimization server may assign a uniquetour ID 1352 for the dataset 1350.

The customized tour optimization server may plan one or more alternativetour routes based on information pertaining to tour subject indicators,including their relevance values associated to one or more locations ofthe mapped space or contents about tour subjects. In an aspect, therelevance values may be weighted by one or more interest factors of theuser(s), where an interest factor indicates a user's level of interestin an indicated subject. In an aspect, the relevance values may beadjusted or change along the tour route(s). In addition, the customizedtour optimization server may include affinity, user preference (past orpresent), user requests and other information in tour route planning.For example, with reference back to FIG. 4A, suppose the selected tourroute for Amy (Amy's tour route) in the tour 500 includes the followingnodes (locations/tour subjects/contents) in the graph database: 411,422, 441, 423, 431, 432, and 433. The processor may tag tour locationsand contents about tour subjects along the route in tags 1368. Based onthe tagged nodes, the customized tour optimization server may pick anon-zero integral number ‘N’ of tour subjects and add their names andlocation information (e.g., geolocation coordinates or addresses) to thedata structure 1350 at elements 1362-1364. To pick the intermediatelocations, the server may select from a list of places or tour subjectsby filtering against expressed or implied preferences of the user(s),subject to the constraints that the added tour time should not be morethan an allotted time. In an alternative, or in addition, the server maycalculate a most favorable route using a predictive analytics algorithmtrained over a set of tour and interest data. Once picking the mostfavorable route, the server may populate the relevant elements1362-1364.

In addition to name and geographic location of any tour elements1360-1366, the server may add other semantic tags relating to the toursubjects, for example from a database of tags or other source. In asimilar manner, the server may assign any tag relevant to both the tourlocations and contents and to the user preferences or affinity data.

The tour subject element 1359 defines one or more locations or visibleplaces relevant to each location elements 1360-1366. The location orvisible places include any object, item, building, structure, landscape,landmark, film studios, filming locations, stores, restaurants, etc.,identified by the server or a client device to occur or exist withrespect to an intermediate location along the route of the tour 500 at aparticular time. The “duration until tour content” element 1356 is ofhigh interest to production of customized tour, which is aforward-looking operation unconcerned with the past. To prepare andoptimize the customized tour in real time, the server needs to know howmuch time will pass until the tour reaches its final destination orpasses an intermediate location for which the server will select one ormore tour content components. Tour contents or events may include, forexample, media contents and scripts (“spiel”) such as Article 443, “TheShow ‘B’ Stage” Spiel 442, for which content durations are predeterminedand known, for which estimated content durations and in turn the overalltour duration can then be calculated at any point along the tour route.Duration until other tour subject 1356 may change during travel (e.g.,due to weather or traffic conditions, or how long the actual tourcontents may take to complete by, e.g., a human tour guide), and may beupdated in the data structure 1350 as tour progresses.

Tour subjects can apply to specific identified places or to a changingset of the locations based on progress of tour. For example, referringback to FIG. 5, at the beginning of the tour (e.g., Amy's tour route500), the server may calculate “Subject A” for intermediate Location X1,“Subject B” for intermediate Location X2, and “Subject n” forintermediate Location Xn for the remaining destination until the tourterminal location Xz and add each location and tour content durations(estimated or actual) including transit times and linger times to thedata structure. The server may refresh the duration element 1356 duringa tour, periodically and/or in response to occurrences of tour subjects.For example, once the tour has departed Location X1, the server maycalculate the linger time of the prior tour lingering at the next tourlocation (e.g., X2), or a transit time to Location X2 (e.g., in light ofthe tour location X2 suddenly facing an announced technical problem andabout to become unavailable as a tour destination during the remainderof the tour duration for Amy's tour), and recalculate the “arrive at”duration for the remaining destinations. In between triggering eventssuch as lingering times at intermediate locations and availability oftour locations, the server may update the “duration until” elementperiodically, for example, every 5 or 10 seconds. In addition, theserver may change, add, or remove intermediate locations and add orremove semantic tags associated with each location or tour contents, asthe tour progresses based on user input, biometric data, traffic data,invitation and/or addition of additional users or tour groups (in caseof tandem tours) to join the tour, and any other new availableinformation. Thus, the data structure 1350 can be updated and supportdynamic configuring, assembling and producing of the customized tour andtour contents during tour 500.

Based on the information as described in connection with data structures350 600, 700, 750, and/or 1350, a server may assemble tour content foruse during a tour. For example, FIG. 13C shows a method 1330 forassembling by one or more processors tour content from a set of mediacomponents based on tour subject information. At 1332, the processor mayidentify an applicable media component set for a tour based on interestfactors or personal affinities of the user or a group of users. Forexample, based on one or more records (e.g., in the user profiles)stating the user loves romantic or romantic comedy shows or movies andprefers to see related tour contents, the server may filter outidentifiers for non-pertinent content components from its library orknowledge graph of all available content components. At 1333, the servermay access user selection data and other data, for example by accessingdata 1331 and identifying a user ID, tour ID, and user's indication of alesser subset of tour subjects selected from the ordered arrangement oftour subject indicators, for locating pertinent tour information in adata structure 1350 (FIG. 13B). At 1334, the server may generate abaseline (e.g., initial) assembly plan based on the tour data 1331,which may be contained in the data structure 1350. The server maygenerate the plan based on the tour origin, destination, preferred toursubjects, and other criteria as described elsewhere herein. The plan1335 may include a sequence of tour locations and tour contents with orwithout other media components, arranged in a list or other useful datastructure.

At 1337, the server may determine that a new tour event defined in thedata structure 1350 has been detected by the processor, for example bytracking progress of the media cart along its tour route. If the tourevent is detected, the server may update the assembly plan 1335 based onthe tour event. Tour event may include a user input (e.g., biometricdata 1020: FIG. 10) via sensors 328, 402, etc., or via U/I 324. Forexample, periodically, the grades or relevance values for the linkagesin the electronic data structure (e.g., 350, 1050) such as the graphdatabase may be adjusted based on the choices the users (tour guests)and other people make. In an aspect, for example, post-tour surveys,reviews, or curating may change these grades. Thus, the processor may“learn” from the users and other participants who may access theelectronic data structure of the customized tour optimization process ormethod of the present disclosure.

Although not shown in FIG. 13C, the augmentation process 1338 may usethe most recent copies of the data structures 350, 1350 available. Forexample if an intermediate location or tour subjects of the existingtour data is deleted or changed as a result of the new tour data or tourevent, the server may remove contents/components picked for a deletedlocation or tour subject, add components picked for the new location ortour subject, and if a preference data is deleted or changed via userfeedback or input, the server may remove contents/components picked fora deleted preference, add contents/components picked for the newpreference; the server may then adjust contents/components based onchanges in estimated durations. At 1336, the server may wait for thenext tour event to occur. If at any time or times while waiting 1336 theserver determines, at 1339 that it is ready to assemble and produce thetour content for presentation in the tour, at 1340 it may retrieve thetour content components for assembly, rank components for relevance at1342, and return highest ranked component(s) at 1344. The processor mayrank components using the customized tour optimization algorithmdiscussed above and herein. Then at 1346 the server may assemble thecomponents into the tour content or portion(s) of the tour content, forexample by arranging segments of media in a sequence and/or rendering amedia component (e.g., a model) to produce a media segment.

At 1348, the server may add the assembled contents/components to a cache1370 for delivery to the user of the tour. Content in the cache may bestreamed or delivered in periodic packages to a user interface device onthe user's person or in the media cart of the tour, and thus, producedfor consumption in real time. Once delivered, the server may deletedelivered content from the cache 1370, immediately after delivery,immediately after the tour is completed, or at some later time. At 1380,the server may determine whether the tour session is completed, forexample in response to termination of the tour or termination of thesession by the user(s). Once finished, the server may terminate 1390 thesession.

FIG. 14 shows an example of an algorithm 1400 for combining two or morelesser subsets selected from the ordered arrangement of tour subjectindicators each corresponding to a different user, such as may beexecuted by one or more computer processors. At 1404, a processorreceives two or more distinct lesser subsets 1402 selected by users. At1406, the processor determines whether the subsets have any commonmembers. If the subsets have common members (e.g., if all the users havemade at least one identical selection), then at 1408, the processor addsone or more common members to a data structure (e.g., a list ordatabase) for holding all tour subjects to be covered during the tour.

If the subsets have no common members, or if after adding the commonmembers empty slots remain in the tour itinerary, at 1412 the processormay determine whether any of the subset members are associated with thesame location or locations in the space to be toured. If anyuser-selected members relate to the same location, at 1414 the processormay add those members to the tour itinerary.

If the subsets have no members with common locations, or if after addingthe members with common locations empty slots remain in the touritinerary, at 1418 the processor may randomly pick a user from theparticipating users in the tour. In an alternative, the algorithm mayprefer selection of users based on a scoring system; in other words, theprocessor may rank the participants in a priority order based on anyappropriate factors. Factors may include, for example, price paid forthe tour, credits or points earned from prior purchases or participationin sponsored activities, or other agreed-upon conditions for granting apreference.

At 1420, the processor adds the highest-priority subject of the selecteduser to the tour itinerary. If the user has not indicated any priority,the processor may select a subject randomly. At 1422, 1416 and 1410, theprocessor may determine whether the tour itinerary list is full, basedon any applicable limit, e.g., time available for the tour, a maximumnumber of locations to be visited, or a maximum number of subjects to beincluded. When the list is not yet full, the process 1400 branches tothe next process, e.g., to picking the next user at 1426, or anotherbranch as shown in FIG. 14.

At 1424, the processor may compile a tour plan from the tour itinerary,including a sequence of locations to be visited and subjects to becovered at each location. The processor may generate the tour plan 1428in a human-readable format, a machine-readable format, or both. In anaspect, the tour plan 1428 may include an address or other link for thecontent to be presented at each location, and navigation information forguiding the group to each location.

FIG. 15 is a schematic diagram showing features of apparatuses forconfiguring a customized tour of a mapped space, showing example aspectsof an electronic tour guide. In an aspect, the media cart 110 includes auser 1511 (e.g., Amy 402), and a human driver 1541, who may be optionalin case of the media cart 110 being an autonomous vehicle. In an aspect,the content corresponding to locations indicated by the at least onetour route record relevant to subjects referenced by the lesser subsetselected from the ordered arrangement of tour subject indicators, whichmay be provided to the user during the tour 500 in the vehicle 110, mayinclude a virtual presence component 1550 (or 1525). For example, one ormore processors may enable a “tour guide” 1501 of a mixed realityapplication 1500 outside the media cart 110 to virtually present herself1501 a inside the media cart 110, using virtual presence equipment suchas virtual presence cameras 1515, microphone 1525, a holoportationdevice 1545 (e.g., Microsoft Holoportation), and any other suitablevirtual presence equipment communicably connectable to the one or moreprocessors and the media cart 110 (not shown) that may be appreciated bya person skilled in the art. The virtual presence may be provided inreal-time, or it may be pre-recorded. In another aspect, the passenger1501 may be virtually presented as a passenger 1501 b on a virtualdisplay device 1535 (e.g., user interface device 480, 485: FIG. 4A) inthe media cart 110 (e.g., 460), or a user's personal user interfacedevice (e.g., smartphone). While not shown in FIG. 15, it should beappreciated that virtual presence may also work with respect to anindividual such as a human tour guide (e.g., 470) inside the media cart110 and virtually presenting herself outside the media cart 110, e.g.,inside a different vehicle/media cart or another location outside themedia cart 110, using holoportation technology such as the one describedabove. This way, the one or more processors may extend conducting a tourbeyond physical confines of a single media cart 110, and a tandem tourcomprising multiple media carts 110 may be provided by a single tourguide simultaneously.

In accordance with the foregoing, FIG. 16 is a flow chart illustratingaspects of a useful automatic method 1600 for configuring a customizedtour of a mapped space, for example, using an atmosphere of knowledgewith graded linkages which learns from feedback from users, and FIGS.17-19 are flow charts illustrating further optional aspects oroperations 1700-1900 of the method diagrammed in FIG. 16. In an aspectof the disclosure, the method 1600 may be performed by one or moreprocessors at a customized tour optimization server or other serversdescribed herein, and may include other details described elsewhereherein.

At 1610, a processor provides an ordered arrangement of tour subjectindicators configured for output by a user interface device. In anaspect, each of the tour subject indicators is associated by anelectronic data structure to a relevance value for one or more locationsof the mapped space and to content about a subject indicated by the toursubject indicator for delivery at corresponding ones of the one or morelocations. In an aspect, the relevance value indicates a degree ofrelevance of each location of the one or more locations to the each ofthe tour subjects. For example, referring to FIG. 4A, in one aspect, therelevance value 451 between Film ‘A’ scene 441 and the movie, Film‘A’422 is 94%, whereas the relevance value 453 between Actor ‘ε’ 411 andFilm ‘A’ 422 is 95%, whereas the relevance value 454 between Film ‘A’422 and tour location 431 is 96%, and so forth. Other edge values 452and 455-459 within the graph database 400 indicate the degrees ofrelevance of respective connected nodes. Relevance values are forexample only, actual relevance values may differ.

At 1620, the processor receives from the user interface device userselection data from a user indicating a lesser subset selected from theordered arrangement of tour subject indicators. For example, in anaspect, Amy (user 402) in FIG. 4A has selected node 441 “Film ‘A’Scene.”

At 1630, the processor calculates an aggregate relevance score for thelesser subset of tour subject indicators and locations of the mappedspace. For example, in an aspect, the relevance score of Amy's selectionof node 441 is 94% with respect to node 422 (Film ‘A’), and therelevance score of Film ‘A’ to location 431 is 96%. Thus, in an example,the aggregate relevance score for Amy's tour route that includes nodes441, 442, and 431 may be 95%, where AVG( ) is used to calculate theaggregate relevance score, or 85.7% if the edge values are multiplied.

At 1640, the processor defines at least one tour route record comprisingan ordered list of locations being a lesser subset of all locations inthe mapped space that satisfies at least a minimum aggregate relevancescore constraint and a maximum tour duration constraint. For example, inan aspect, the processor may select “Amy's tour route” as described withreference to FIG. 13A above.

At 1650, the processor saves information defining the at least one tourroute record in a computer memory for use in delivering a correspondingtour.

FIGS. 17-19 show additional operations 1700-1900 that may optionally beincluded in the method 1600 and performed by one or more processorsperforming the method 1600, in any operative order. Any one or more ofthe additional operations 1700-1900 may be omitted unless needed by adownstream operation and one or more of the operations 1700-1900 mayreplace one or more others of the operations of the method 1600. Anyuseful combination of operations selected from the group consisting ofthe operations 1700-1900 may be performed by one or more processors ofan apparatus as described herein as an independent method distinct fromother useful combinations selected from the same group.

Referring to FIG. 17, at 1710, the one or more processors perform themethod wherein the calculating further includes aggregating edge valuesof a graph database linking the lesser subset of tour subject indicatorsto the locations of the mapped space, wherein nodes of the graphcomprise the tour subject indicators, for example, as discussed withreference to FIG. 13A above.

At 1720, the one or more processors calculate an estimated tour durationbased on the ordered list of locations, at least in part by summingestimated transit times between each location and estimated linger timesat each location, for example, as discussed with reference to FIG. 13Aabove.

At 1730, the one or more processors estimate the estimated linger timesat the each location at least in part based on a predetermined deliveryperiod for the content for delivery at said each location, for example,as discussed with reference to FIG. 13A above.

At 1740, the one or more processors perform the method wherein thedefining further comprises the calculating the aggregate relevance scorefor each of alternative tour routes satisfying the minimum aggregaterelevance score constraint and the maximum tour duration, for example,as discussed with reference to FIG. 13A above.

At 1750, the one or more processors select a selected route from thealternative tour routes based on the selected route having a greatest ofaggregate relevance scores determined by the calculating, for example,as discussed with reference to FIG. 13A above.

Referring to FIG. 18, at 1810, the one or more processors perform themethod wherein the defining further comprises generating the orderedlist of locations in part by eliminating candidate locations for periodsduring which the candidate location is indicated as unavailable, forexample, as discussed with reference to FIG. 13B above.

At 1820, the one or more processors calculate periods during which thecandidate location is unavailable at least in part based on scheduleduse of the location by one or more prior tours. For example, in case ofAmy's tour route, suppose the tour route includes location 431registered as Location X1 in tour route records 600 and/or other datastructure (e.g., 350, 1350, etc.), and further suppose that the tourduration requested by Amy is between 13:00 to 15:00. Further supposethat a prior tour that is occurring before Amy's requested tour time isscheduled to visit and use location 431 as part of that tour between13:15 and 13:30. In such instance, the processor may calculate thatlocation 431 is unavailable for Amy's tour between 13:15 and 13:30, butnot between 13:00 to 13:15, and 13:30 to 15:00. Of course, additionaltimes may be added or subtracted from this unavailability calculation,for instance, by taking into account estimated or actual linger timesand transit times for the respective location/tour content.

At 1830, the one or more processors generate an electronic tour guidefor content corresponding to locations indicated by the at least onetour route record relevant to subjects referenced by the lesser subsetselected from the ordered arrangement of tour subject indicators, forexample, as described with reference to FIG. 15.

At 1840, the one or more processors provide the electronic tour guide toat least one of a mobile device belonging to the user, or a mobiledevice belonging to a tour guide, for example, as described withreference to FIG. 15.

At 1850, the one or more processors combine two or more lesser subsetsselected from the ordered arrangement of tour subject indicators eachcorresponding to a different user. Combination may occur using variousalgorithms. For example, at 1860, the one or more processors select thetwo or more lesser subsets based on a similarity measure exceeding athreshold value. An example is provided at 1400 of FIG. 14. For furtherexample, the two or more lesser subsets may be lists created bydifferent users of tour subjects they are interested in exploring duringthe tour. In an aspect, the similarity measure, as may be understood bya person of ordinary skill in the art, is a way of measuring how muchalike two data objects (e.g., two or more lesser subsets of the toursubject indicators) are. Additional discussions about similaritymeasures is discussed in detail in Polamuri, Saimadhu, “Five MostPopular Similarity Measures Implementation In Python”http://dataaspirant.com/2015/04/11/five-most-popular-similarity-measures-implementation-in-python/,which is hereby incorporated by reference in its entirety. For example,in an aspect, if two different tour lists are similar enough, the twousers should be grouped together on the same tour, otherwise they wouldtake different tours. For a studio lot tour, whether the subjectsconcern the same location may be determined by the processor as the mostheavily weighted factor in the similarity measure.

For further example, at 1870, the one or more processors may prioritizetour subject indicators in the two or more lesser subsets for includingin the at least one tour route record, at least in part by an algorithmthat provides each participating user with equal consideration inselection of unique tour subject indicators. For example, in the specialcase wherein available slots in the tour route are equal to, or aninteger multiple of, participating users, the algorithm selects an equalnumber of unique tour subject indicators for each participating user.For example, if the available slots are less than the number of users,the algorithm may maximize the utility of the tour by selecting toursubject indicators on a democratic basis, in order of the number ofusers indicating each subject. Where an equal number of users indicate agreater number of subjects than available slots, the algorithm mayrandomly select tour subjects while preventing selections that award anyuser with more than one additional subject compared to other users. Inan alternative, the algorithm may prefer selections based on competitivefactors, for example price paid for the tour or credits earned fromauxiliary activities, e.g., store purchases or participation insponsored activities.

Referring to FIG. 19, at 1910, for each of the tour subject indicators,the one or more processors weight the relevance value by an interestfactor indicating a user's level of interest in the indicated subject.For example, as discussed with reference to FIGS. 7A-7B, and 8 above.

At 1920, the one or more processors determine the interest factor basedon one or more of user input, prior user feedback for a cohort matchingthe user, or electronic data indicating user preferences. For example, acohort matching the user may be determined based on user affinity orinterest factors of the user and the cohort.

At 1930, the one or more processors modify the at least one tour recordduring the tour, based at least in part on the user feedback, forexample, as discussed with reference to FIG. 13C.

At 1940, the one or more processors receive at least a portion of theuser feedback as biometric data indicating a neurological response ofthe user to delivery of the content, from a biometric sensor positionedto detect the neurological response. For example, in some aspects, theprocessor may monitor brain activity of a user with a user device. Forexample, a user device (e.g., configured as headwear) may incorporate anEEG, EMG, or another component capable of monitoring brain activity. Inanother example, the user device may receive brain activity data from acomponent capable of monitoring brain activity that is not incorporatedinto the user device. Detection of a neurological response may be asdescribed in detail in international Patent App. No. PCT/US18/53614which is incorporated herein by reference.

At 1950, the one or more processors adjust at least one of the relevancevalue or interest factor based on user feedback received during or aftercompleting at least a portion of a tour following the tour route record.

At 1960, the one or more processors track progress of the user along thetour, at least in part by receiving a signal from a wireless transmitterprogressing through the tour with the user.

At 1970, the one or more processors process the user feedback by amachine-learning algorithm trained to predict at least one of theinterest factor or the relevance value for specific user cohorts.

Referring to FIG. 20, components of an apparatus or system 2000 forconfiguring a customized tour of a mapped space are illustrated,according to one embodiment. The apparatus or system 2000 may includeadditional or more detailed components for performing functions orprocess operations as described herein. As depicted, the apparatus orsystem 2000 may include functional blocks that can represent functionsimplemented by a processor, software, or combination thereof (e.g.,firmware).

As illustrated in FIG. 20, the apparatus or system 2000 may include anelectrical component 2002 for providing an ordered arrangement of toursubject indicators configured for output by a user interface device,wherein each of the tour subject indicators is associated by anelectronic data structure to a relevance value for one or more locationsof the mapped space and to content about a subject indicated by the toursubject indicator for delivery at corresponding ones of the one or morelocations, wherein the relevance value indicates a degree of relevanceof each location of the one or more locations to the each of the toursubjects. The component 2002 may be, or may include, a means for saidproviding. Said means may include the processor 2010 coupled to thememory 2016, the processor executing an algorithm based on programinstructions stored in the memory. Optionally, the means may include oneor more of a network interface 2011, a biometric sensor (array) 2014, anoutput port 2012, and a bus 2013. In an aspect, the tour subjectindicators may be received from external systems via the networkinterface (not shown). Such algorithm may include a sequence of moredetailed operations, for example, establishing a communication sessionwith a customized tour optimization server, and at least one ofstreaming or pushing the tour subject indicators to the user interfacedevice for output to the user.

The apparatus 2000 may further include an electrical component 2003 forreceiving from the user interface device, user selection data from auser indicating a lesser subset selected from the ordered arrangement oftour subject indicators. The component 2003 may be, or may include, ameans for said receiving. Said means may include the processor 2010coupled to the memory 2016, the processor executing an algorithm basedon program instructions stored in the memory. Such algorithm may includea sequence of more detailed operations, for example, establishing acommunication session with a user interface device, and requestingand/or receiving the information about user selection of one or moretour subject indicators selectable by the user or users using one ormore user interface devices. Optionally, the means may include one ormore of the network interface 2011, the biometric sensor (array) 2014,the output port 2012, and the bus 2013.

The apparatus 2000 may further include an electrical component 2004 forcalculating an aggregate relevance score for the lesser subset of toursubject indicators and locations of the mapped space. The component 2004may be, or may include, a means for said calculating. Said means mayinclude the processor 2010 coupled to the memory 2016, the processorexecuting an algorithm based on program instructions stored in thememory. Such algorithm may include a sequence of more detailedoperations, for example, as shown in FIGS. 4A-4B including amachine-learning or rules-based algorithm.

The apparatus 2000 may further include an electrical component 2005 fordefining at least one tour route record comprising an ordered list oflocations being a lesser subset of all locations in the mapped spacethat satisfies at least a minimum aggregate relevance score constraintand a maximum tour duration constraint. The component 2005 may be, ormay include, a means for said defining. Said means may include theprocessor 2010 coupled to the memory 2016 the processor executing analgorithm based on program instructions stored in the memory. Suchalgorithm may include a sequence of more detailed operations, forexample, as shown in FIG. 13C including a machine-learning orrules-based algorithm. Optionally, the means may include one or more ofa network interface 2011, a biometric sensor (array) 2014, an outputport 2012, and a bus 2013.

The 2000 may further include an electrical component 2006 for savinginformation defining the at least one tour route record in a computermemory for use in delivering a corresponding tour. The component 2006may be, or may include, a means for said saving. Said means may includethe processor 2010 coupled to the memory 2016, the processor executingan algorithm based on program instructions stored in the memory. Suchalgorithm may include a sequence of more detailed operations, forexample, formatting output of a selection algorithm as one or more touritinerary files, and sending the files to a file storage subsystem thatsaves the files in a compute memory.

The apparatus 2000 may optionally include a processor module 2010 havingat least one processor. The processor 2010 may be in operativecommunication with the modules 2002-2005 via a bus 2013 or similarcommunication coupling. In the alternative, one or more of the modulesmay be instantiated as functional modules in a memory of the processor.The processor 2010 may initialize and schedule the processes orfunctions performed by electrical components 2002-2005.

In related aspects, the apparatus 2000 may include a network interfacemodule 2011 operable for communicating with system components over acomputer network, or communicating with any external storage device,with external systems or servers, or connected vehicles over a computernetwork. A network interface 2011 module may be, or may include, forexample, an Ethernet port or serial port (e.g., a Universal Serial Bus(USB) port), a Wi-Fi interface, or a cellular telephone interface. Infurther related aspects, the apparatus 2000 may optionally include amodule for storing information, such as, for example, a memory device2016. The computer readable medium or the memory module 2016 may beoperatively coupled to the other components of the apparatus 2000 viathe bus 2013 or the like. The memory module 2016 may be adapted to storecomputer readable instructions and data for effecting the processes andbehavior of the modules 2002-2005, and subcomponents thereof, or theprocessor 2010, the method 1600 and one or more of the additionaloperations 1700-1900 disclosed herein, or any method for performance byan interactive media content output device (interactive media player)described herein. The memory module 2016 may retain instructions forexecuting functions associated with the modules 2002-2005 and any one ormore of the operations described herein, for example in connection withone or more of FIGS. 5-19. While shown as being external to the memory2016, it is to be understood that the modules 2002-2005 can exist withinthe memory 2016 or an on-chip memory of the processor 2010.

The apparatus 2000 may include a transceiver configured as a wirelesstransmitter/receiver, or a wired transmitter/receiver, for transmittingand receiving a communication signal to/from another system componentsuch as, for example, an RFID tag or location information transmitter.In alternative embodiments, the processor 2010 may include networkedmicroprocessors from devices operating over a computer network. Inaddition, the apparatus 2000 may include a stereoscopic display or otherimmersive display device for displaying immersive content, or othersuitable output device. A stereoscopic display device may be, or mayinclude, any suitable stereoscopic AR or VR output device as known inthe art. The apparatus 2000 may include, or may be connected to, one ormore biometric sensors 2014, which may be of any suitable types. Variousexamples of suitable biometric sensors are described herein above.

Those of skill would further appreciate that the various illustrativelogical blocks, modules, circuits, and algorithm steps described inconnection with the aspects disclosed herein may be implemented aselectronic hardware, computer software, or combinations of both. Toclearly illustrate this interchangeability of hardware and software,various illustrative components, blocks, modules, circuits, and stepshave been described above generally in terms of their functionality.Whether such functionality is implemented as hardware or softwaredepends upon the particular application and design constraints imposedon the overall system. Skilled artisans may implement the describedfunctionality in varying ways for each particular application, but suchimplementation decisions should not be interpreted as causing adeparture from the scope of the present disclosure.

As used in this application, the terms “component”, “module”, “system”,and the like are intended to refer to a computer-related entity, eitherhardware, a combination of hardware and software, software, or softwarein execution. For example, a component or a module may be, but are notlimited to being, a process running on a processor, a processor, anobject, an executable, a thread of execution, a program, and/or acomputer. By way of illustration, both an application running on aserver and the server can be a component or a module. One or morecomponents or modules may reside within a process and/or thread ofexecution and a component or module may be localized on one computerand/or distributed between two or more computers.

Various aspects will be presented in terms of systems that may includeseveral components, modules, and the like. It is to be understood andappreciated that the various systems may include additional components,modules, etc. and/or may not include all the components, modules, etc.discussed in connection with the figures. A combination of theseapproaches may also be used. The various aspects disclosed herein can beperformed on electrical devices including devices that utilize touchscreen display technologies, heads-up user interfaces, wearableinterfaces, and/or mouse-and-keyboard type interfaces. Examples of suchdevices include VR output devices (e.g., VR headsets), AR output devices(e.g., AR headsets), computers (desktop and mobile), televisions,digital projectors, smart phones, personal digital assistants (PDAs),and other electronic devices both wired and wireless.

In addition, the various illustrative logical blocks, modules, andcircuits described in connection with the aspects disclosed herein maybe implemented or performed with any suitable hardware processor,including for example a general purpose processor, a digital signalprocessor (DSP), an application specific integrated circuit (ASIC), afield programmable gate array (FPGA) or other programmable logic device(PLD) or complex PLD (CPLD), discrete gate or transistor logic, discretehardware components, or any combination thereof designed to perform thefunctions described herein. A general-purpose processor may be amicroprocessor, but in the alternative, the processor may be anyconventional processor, controller, microcontroller, or state machine. Aprocessor may also be implemented as a combination of computing devices,e.g., a combination of a DSP and a microprocessor, a plurality ofmicroprocessors, one or more microprocessors in conjunction with a DSPcore, or any other such configuration.

Operational aspects disclosed herein may be embodied directly inhardware, in a software module executed by a processor, or in acombination of the two. A software module may reside in RAM memory,flash memory, ROM memory, EPROM memory, EEPROM memory, registers, harddisk, a removable disk, a CD-ROM, digital versatile disk (DVD),Blu-Ray™, or any other form of computer-readable memory known in theart. An exemplary storage medium is coupled to the processor such theprocessor can read information from, and write information to, thestorage medium. In the alternative, the storage medium may be integralto the processor. The processor and the storage medium may reside in anASIC. The ASIC may reside in a client device or server. In thealternative, the processor and the storage medium may reside as discretecomponents in a client device or server.

Furthermore, the one or more versions may be implemented as a method,apparatus, or article of manufacture using standard programming and/orengineering techniques to produce software, firmware, hardware, or anycombination thereof to control a computer to implement the disclosedaspects. Non-transitory computer readable media can include but are notlimited to magnetic storage devices (e.g., hard disk, floppy disk,magnetic strips, or other format), optical disks (e.g., compact disk(CD), DVD, Blu-Ray™ or other format), smart cards, and flash memorydevices (e.g., card, stick, or other formats). Of course, those skilledin the art will recognize many modifications may be made to thisconfiguration without departing from the scope of the disclosed aspects.

The previous description of the disclosed aspects is provided to enableany person skilled in the art to make or use the present disclosure.Various modifications to these aspects will be readily apparent to thoseskilled in the art, and the generic principles defined herein may beapplied to other embodiments without departing from the spirit or scopeof the disclosure. Thus, the present disclosure is not intended to belimited to the embodiments shown herein but is to be accorded the widestscope consistent with the principles and novel features disclosedherein.

In view of the exemplary systems described supra, methodologies that maybe implemented in accordance with the disclosed subject matter have beendescribed with reference to several flow diagrams. While for purposes ofsimplicity of explanation, the methodologies are shown and described asa series of blocks, it is to be understood and appreciated that theclaimed subject matter is not limited by the order of the blocks, assome blocks may occur in different orders and/or concurrently with otherblocks from what is depicted and described herein. Moreover, not allillustrated blocks may be required to implement the methodologiesdescribed herein. Additionally, it should be further appreciated thatthe methodologies disclosed herein are capable of being stored on anarticle of manufacture to facilitate transporting and transferring suchmethodologies to computers.

1. A computer-implemented method for configuring a customized tour of amapped space, the method comprising: providing, by one or moreprocessors, an ordered arrangement of tour subject indicators configuredfor output by a user interface device, wherein each of the tour subjectindicators is associated by an electronic data structure to a relevancevalue for one or more locations of the mapped space and to content abouta subject indicated by the tour subject indicator for delivery atcorresponding ones of the one or more locations, wherein the relevancevalue indicates a degree of relevance of each location of the one ormore locations to the each of the tour subjects; receiving, by the oneor more processors from the user interface device, user selection datafrom a user indicating a lesser subset selected from the orderedarrangement of tour subject indicators; calculating, by the one or moreprocessors, an aggregate relevance score for the lesser subset of toursubject indicators and locations of the mapped space; defining, by theone or more processors, at least one tour route record comprising anordered list of locations being a lesser subset of all locations in themapped space that satisfies at least a minimum aggregate relevance scoreconstraint and a maximum tour duration constraint; and savinginformation defining the at least one tour route record in a computermemory for use in delivering a corresponding tour.
 2. The method ofclaim 1, wherein the calculating further comprises aggregating edgevalues of a graph database linking the lesser subset of tour subjectindicators to the locations of the mapped space, wherein nodes of thegraph comprise the tour subject indicators.
 3. The method of claim 1further comprising, by the one or more processors, calculating anestimated tour duration based on the ordered list of locations, at leastin part by summing estimated transit times between each location andestimated linger times at each location.
 4. The method of claim 3,further comprising estimating, by the one or more processors, theestimated linger times at the each location at least in part based on apredetermined delivery period for the content for delivery at the eachlocation.
 5. The method of claim 1, wherein the defining furthercomprises the calculating the aggregate relevance score for each ofalternative tour routes satisfying the minimum aggregate relevance scoreconstraint and the maximum tour duration.
 6. The method of claim 5,further comprising selecting, by the one or more processors, a selectedroute from the alternative tour routes based on the selected routehaving a greatest of aggregate relevance scores determined by thecalculating.
 7. The method of claim 1, wherein the defining furthercomprises generating the ordered list of locations in part byeliminating candidate locations for periods during which the candidatelocation is indicated as unavailable.
 8. The method of claim 7, furthercomprising calculating, by the one or more processors, periods duringwhich the candidate location is unavailable at least in part based onscheduled use of the location by one or more prior tours.
 9. The methodof claim 1, further comprising by the one or more processors for each ofthe tour subject indicators, weighting the relevance value by aninterest factor indicating a user's level of interest in the indicatedsubject.
 10. The method of claim 9, further comprising, by the one ormore processors, determining the interest factor based on one or more ofuser input, prior user feedback for a cohort matching the user, orelectronic data indicating user preferences.
 11. The method of claim 9,further comprising, by the one or more processors, adjusting at leastone of the relevance value or interest factor based on user feedbackreceived during or after completing at least a portion of a tourfollowing the tour route record.
 12. The method of claim 9, furthercomprising tracking, by the one or more processors, progress of the useralong the tour, at least in part by receiving a signal from a wirelesstransmitter progressing through the tour with the user.
 13. The methodof claim 10, further comprising modifying the at least one tour recordduring the tour, based at least in part on the user feedback.
 14. Themethod of claim 10, further comprising receiving at least a portion ofthe user feedback as biometric data indicating a neurological responseof the user to delivery of the content, from a biometric sensorpositioned to detect the neurological response.
 15. The method of claim9, further comprising, by the one or more processors, processing theuser feedback by a machine-learning algorithm trained to predict atleast one of the interest factor or the relevance value for specificuser cohorts.
 16. The method of claim 1, further comprising generatingan electronic tour guide for content corresponding to locationsindicated by the at least one tour route record relevant to subjectsreferenced by the lesser subset selected from the ordered arrangement oftour subject indicators.
 17. The method of claim 16, further comprisingproviding the electronic tour guide to at least one of a mobile devicebelonging to the user, or a mobile device belonging to a tour guide. 18.The method of claim 1 further comprising combining, by the one or moreprocessors, two or more lesser subsets selected from the orderedarrangement of tour subject indicators each corresponding to a differentuser.
 19. The method of claim 18 further comprising, by the one or moreprocessors, selecting the two or more lesser subsets based on asimilarity measure exceeding a threshold value.
 20. The method of claim18 further comprising, by the one or more processors, prioritizing toursubject indicators in the two or more lesser subsets for including inthe at least one tour route record, at least in part by an algorithmthat includes an equal number of unique tour subject indicators for eachparticipating user.
 21. An apparatus for configuring a customized tourof a mapped space, the apparatus comprising at least one processorcoupled to a memory, the memory holding program instructions that whenexecuted by the at least one processor cause the apparatus to perform:providing an ordered arrangement of tour subject indicators configuredfor output by a user interface device, wherein each of the tour subjectindicators is associated by an electronic data structure to a relevancevalue for one or more locations of the mapped space and to content abouta subject indicated by the tour subject indicator for delivery atcorresponding ones of the one or more locations, wherein the relevancevalue indicates a degree of relevance of each location of the one ormore locations to the each of the tour subjects; receiving userselection data from a user indicating a lesser subset selected from theordered arrangement of tour subject indicators; calculating an aggregaterelevance score for the lesser subset of tour subject indicators andlocations of the mapped space; defining at least one tour route recordcomprising an ordered list of locations being a lesser subset of alllocations in the mapped space that satisfies at least a minimumaggregate relevance score constraint and a maximum tour durationconstraint; and saving information defining the at least one tour routerecord in the memory for use in delivering a corresponding tour.
 22. Theapparatus of claim 21, wherein the memory holds further instructions forperforming the calculating at in part by aggregating edge values of agraph database linking the lesser subset of tour subject indicators tothe locations of the mapped space, wherein nodes of the graph comprisethe tour subject indicators.
 23. The apparatus of claim 21, wherein thememory holds further instructions for calculating an estimated tourduration based on the ordered list of locations, at least in part bysumming estimated transit times between each location and estimatedlinger times at each location.
 24. The apparatus of claim 23, whereinthe memory holds further instructions for estimating the estimatedlinger times at the each location at least in part based on apredetermined delivery period for the content for delivery at the eachlocation.
 25. The apparatus of claim 21, wherein the memory holdsfurther instructions for performing the defining at least in part bycalculating the aggregate relevance score for each of alternative tourroutes satisfying the minimum aggregate relevance score constraint andthe maximum tour duration.
 26. The apparatus of claim 25, wherein thememory holds further instructions for selecting a selected route fromthe alternative tour routes based on the selected route having agreatest of aggregate relevance scores determined by the calculating.27. The apparatus of claim 21, wherein the memory holds furtherinstructions for the defining at least in part by eliminating candidatelocations from the ordered list of locations for periods during whichthe candidate location is indicated as unavailable.
 28. The apparatus ofclaim 27, wherein the memory holds further instructions for calculatingperiods during which the candidate location is unavailable at least inpart based on scheduled use of the location by one or more prior tours.29. The apparatus of claim 21, wherein the memory holds furtherinstructions for weighting, for each of the tour subject indicators, therelevance value by an interest factor indicating a user's level ofinterest in the indicated subject.
 30. The apparatus of claim 29,wherein the memory holds further instructions for determining theinterest factor based on one or more of user input, prior user feedbackfor a cohort matching the user, or electronic data indicating userpreferences.
 31. The apparatus of claim 29, wherein the memory holdsfurther instructions for adjusting at least one of the relevance valueor interest factor based on user feedback received during or aftercompleting at least a portion of a tour following the tour route record.32. The apparatus of claim 29, wherein the memory holds furtherinstructions for tracking progress of the user along the tour, at leastin part by receiving a signal from a wireless transmitter progressingthrough the tour with the user.
 33. The apparatus of claim 30, whereinthe memory holds further instructions for modifying the at least onetour record during the tour, based at least in part on the userfeedback.
 34. The apparatus of claim 30, wherein the memory holdsfurther instructions for receiving at least a portion of the userfeedback as biometric data indicating a neurological response of theuser to delivery of the content, from a biometric sensor positioned todetect the neurological response.
 35. The apparatus of claim 29, whereinthe memory holds further instructions for processing the user feedbackby a machine-learning algorithm trained to predict at least one of theinterest factor or the relevance value for specific user cohorts. 36.The apparatus of claim 21, wherein the memory holds further instructionsfor generating an electronic tour guide for content corresponding tolocations indicated by the at least one tour route record relevant tosubjects referenced by the lesser subset selected from the orderedarrangement of tour subject indicators.
 37. The apparatus of claim 36,wherein the memory holds further instructions for providing theelectronic tour guide to at least one of a mobile device belonging tothe user, or a mobile device belonging to a tour guide.
 38. Theapparatus of claim 21, wherein the memory holds further instructions forcombining two or more lesser subsets selected from the orderedarrangement of tour subject indicators each corresponding to a differentuser.
 39. The apparatus of claim 38, wherein the memory holds furtherinstructions for selecting the two or more lesser subsets based on asimilarity measure exceeding a threshold value.
 40. The apparatus ofclaim 38, wherein the memory holds further instructions for,prioritizing tour subject indicators in the two or more lesser subsetsfor including in the at least one tour route record, at least in part byan algorithm that provides each participating user with equalconsideration in selection of unique tour subject indicators.
 41. Anapparatus for configuring a customized tour of a mapped space, theapparatus comprising: means for providing an ordered arrangement of toursubject indicators configured for output by a user interface device,wherein each of the tour subject indicators is associated by anelectronic data structure to a relevance value for one or more locationsof the mapped space and to content about a subject indicated by the toursubject indicator for delivery at corresponding ones of the one or morelocations, wherein the relevance value indicates a degree of relevanceof each location of the one or more locations to the each of the toursubjects; means for receiving user selection data from a user indicatinga lesser subset selected from the ordered arrangement of tour subjectindicators; means for calculating an aggregate relevance score for thelesser subset of tour subject indicators and locations of the mappedspace; means for defining at least one tour route record comprising anordered list of locations being a lesser subset of all locations in themapped space that satisfies at least a minimum aggregate relevance scoreconstraint and a maximum tour duration constraint; and means for savinginformation defining the at least one tour route record in the memoryfor use in delivering a corresponding tour.